• UK workers are prepared to use AI so long as its benefits are also shared with them
• Employees worry they don't have enough time and resources to keep up with skills
• Companies should be more transparent in supporting their workers

One in three UK workers would expect what insurance group Howden describes as an 'AI dividend' – a higher pay – if their employer were able to achieve significant productivity gains or cost savings through the use of AI.

The study of over 2,000 employees found a general acceptance toward AI, so long as its benefits are also passed onto them and not just the company.

Those impacted by AI would also expect shorter working hours (16%), greater job security (15%) and a bonus (13%) if their company managed to unlock AI's benefits.

Workers want to benefit from AI as much as their employers

Despite an appetite for AI's benefits to be passed onto them, half (49%) worry they could be expected to complete even more work because of the time savings and another half (50%) fear AI could soon be used to assess their performance. A separate study has already demonstrated how workers feel pressured to use AI, showing that they use it regardless of its benefits just to keep managers happy.

But before all of this becomes a reality, many companies still need to roll out AI across workers' environments. One on two worry it could be introduced faster than they understand it and 53% are worried they won't have time to learn the new skills on top of their existing workloads.

The report found that simply providing access to AI training isn't enough – workers need fully support, including dedicated learning time.

"With the workforce being central to the AI-productivity journey, our research show’s there’s a misalignment in expectations that must be addressed to improve the chances of success," Howden Employee Benefits & Wellbeing MD Cheryl Brennan wrote.

The UK Government has already set out plans to upskill 10 million workers in AI by 2030, but uptake has reportedly been low and Howden is reminding employers that they must also play a role in their duty to workers.

Brennan concluded with the need for "dialogue and action regarding pay, progression and transparency," keeping human workers in the loop and granting them access to AI's benefits.

Whether you're all in on AI or staunchly opposed to it, the new Channel 4 documentary on the topic will be sure to get your attention. Directed by Nick Holt, this two-hour long doc charts the origins and immense rise of artificial intelligence over the last few years, with a focus on the work of Geoffrey Hinton, dubbed the godfather of AI.

His AI model started to learn autonomously, sparking the technological revolution we are now living in, for better or worse. The doc looks at the human impact, as well as the way corporations are buying into the technology, particularly people like Demis Hassabis, Sam Altman, and Elon Musk.

Hinton himself is deeply troubled and concerned by what he created, and it is that irony that frames the narrative of the documentary.

Ready to learn more about the success and dangers of AI? Read on to see how to watch AI: Probably Nothing to Worry About from anywhere and potentially for free.

How to watch AI: Probably Nothing to Worry About for free in the UK

In the UK, AI: Probably Nothing to Worry About will air for FREE on Channel 4 on Thursday, November 12, 2026 at 10pm.

If you miss the broadcast you'll be able to watch the doc on Channel 4's streaming platform afterwards.

How to watch AI doc from abroad with a VPN

TechRadar regularly reviews all the biggest and best VPN providers and NordVPN is our #1 choice.

Can you watch AI: Probably Nothing to Worry About in the US?

AI: Probably Nothing to Worry About is not getting a release in the US just yet, be it on TV or streaming services.

The film did have a run at Tribeca Film Festival earlier this year, though, and it is likely to arrive in the US at some point.

Away from home? Use a VPN to watch the doc when traveling around – it's easy. Details above..

Can you watch AI: Probably Nothing to Worry About in Canada?

Likewise, there are no plans to release AI: Probably Nothing to Worry About in Canada at the moment, be it on normal TV or a streaming platform.

UK viewer currently traveling in Canada? Download a VPN to connect to your streaming service back home and watch AI: Probably Nothing to Worry About.

Can I watch AI: Probably Nothing to Worry About in Australia?

Similarly, there is nothing to suggest that AI: Probably Nothing to Worry About will get a release in Australia at this time.

Aussies can also use a VPN to get access to watch UK TV from abroad.

Who's involved with AI: Probably Nothing to Worry About?

• Geoffrey Hinton (interview subject)
• Demis Hassabis (interview subject)
• Former OpenAI staff (interview subjects)
• Vitalik Buterin (interview subjects)
• Emad Mostaque (interview subjects)
• Nick Holt (director)
• Zara Powell, Ben Brown, David Dugan, Mark Raphael, David Glover (producers)
• Barbara Broccoli, David Glover, David Dugan, Mark Raphael (executive producers)
• Jack Brophy, Stefan Wiesen, Duane McClunie (cinematographers)
• Sarah Playford (composer)
• Ben Brown (editor)
• 72 Films, Windfall Films, FremantleMedia (production companies)

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• Several firms say almost no one is using AI agents to access their services
• Uber, DoorDash and Airbnb all say the numbers are comically low
• Users have pushed back against AI agents and questioned their necessity

The whole point of using an AI agent, whether that’s Meta’s Muse or OpenAI’s Dots, is that it will save you time, change the way you work and perform the tedious drudgery that you’d rather not do. But several firms, including Uber, DoorDash and Airbnb, have admitted that almost no one is using AI agents to access their services, suggesting that the agentic revolution we were all promised might not be materializing — for now, at least.

The three firms said (via TechSpot) that AI agents had only been used to perform a small handful of transactions in recent weeks, compared to the millions carried out by real people. Uber, for example, had 320 million car rides booked by humans during the week of Halloween. The amount placed by AI agents? Just four, an almost comically low number.

DoorDash CEO Tony Xu shared a similar experience, saying that AI agents were responsible for several hundred orders on the platform. While the numbers aren’t as low as on Uber, TechSpot noted that DoorDash receives billions of orders every year.

And Airbnb CEO Brian Chesky argued that only a negligible portion of bookings on the app are made using AI agents. Chesky surmised that this might be because many people like to research trips themselves rather than hand everything over to an AI that can’t gauge the quality of a rental or the attractiveness of things to do nearby.

The AI agent backlash?

Part of the problem could be that AI agents are, in some cases, a solution in search of a problem. For instance, Uber’s chief financial officer Balaji Krishnamurthy stated that it takes people around 50 seconds to book a ride on the service, so it’s not exactly a longwinded process that AI agents can dramatically simplify.

There’s been something of a backlash on social media, too. On Reddit, one highly-upvoted comment noted that "thinking about what you feel like doing or eating is fun" and that few people are looking to become "hyper-efficient at having fun". An AI agent, they argued, sucks the joy out of life’s seemingly-mundane pursuits.

Another user put it this way: “So instead of opening the DoorDash app to order a pizza, I have to open my AI Agent and tell them I’m hungry and want to eat pizza and it has to be less than $30? Seems like more work to me to use [an] AI Agent.”

Indeed, a recent report found that far from saving you time, AI tools are forcing many people to work an extra 90 minutes a day, while a separate study noted that workers are spending more time correcting AI content than doing actual work. Combined with the latest news from Uber, DoorDash and Airbnb, there's a growing sense that AI isn't always the time-saving solution it might appear on the surface.

Several companies and governments have already had enough of AI agents. The Wall Street Journal reports that United Airlines is banning AI bots from booking flights, while Alibaba has stopped its workers from using Claude Code and the Swedish government has blocked both Claude and ChatGPT over security concerns.

Perhaps one day AI agents will take off and everyone will be using them to streamline mundane tasks. But on the latest evidence, that day is a long way off — and there's a feeling that not every task is necessarily ripe for automation.

Former Google CEO Eric Schmidt thinks AI struggles to convince Americans that it's a net good because of its messaging. After the U.S. election exposed growing public hostility toward AI, particularly the data centers necessary to power the technology, Schmidt argued in an Axios interview that the companies have done a poor job explaining the benefits of their products, but he is underestimating how much people already understand about AI.

"Our industry does a terrible job of marketing our benefits," Schmidt said. "We talk about all the negatives without talking the positives, and people are much more likely to index their minds on the negatives."

Millions have experienced ChatGPT and Google's Gemini firsthand for good and ill. They're perfectly capable of appreciating those benefits while remaining unhappy about the industry's growing influence over their lives.

Product or pitch?

AI data centers became increasingly contentious during the November midterms, with voters frustrated by electricity costs and the environmental consequences of large computing facilities. What was once largely a planning dispute has become a political headache for an industry accustomed to presenting technological expansion as an unquestionable public good. Schmidt's solution is to move the data centers.

"Let me state categorically that large data centers should not be near population centers," Schmidt said. "Why would this be controversial? And they should have their own power systems. One of the great things about America is we have an awful lot of empty spaces. Put them there."

But Schmidt's argument still treats public skepticism as something better marketing might overcome. People worried about rising electricity bills aren't necessarily confused about what AI can accomplish. They may simply believe that cheaper access to increasingly powerful chatbots isn't worth paying more to keep the lights on.

The same applies to concerns about employment. A more polished explanation of AI's productivity benefits won't necessarily reassure someone whose employer is considering replacing part of their workload with software.

Skepticism over the utopian vision

Schmidt remains deeply optimistic about AI's potential, particularly in scientific research. He expects researchers to work alongside increasingly capable AI agents, accelerating discoveries that might otherwise take considerably longer. If AI delivers major advances in medicine or scientific research, people may feel more positively disposed toward the technology. But promised breakthroughs don't automatically offset the problems people are experiencing today.

There's also something slightly absurd about an industry with enormous marketing budgets complaining that Americans haven't heard enough about AI's benefits. ChatGPT has become a household name, while Google and Microsoft have spent years placing AI features in products people already use. The public has had plenty of opportunities to hear how revolutionary the technology is supposed to be.

Public trust can't be manufactured entirely through better messaging, especially when people legitimately disagree with the industry's priorities. Sometimes skepticism reflects a reasonably informed judgment about a technology's consequences.

Schmidt clearly believes the future made with AI will be worth the disruption required to reach it, but Americans don't necessarily need another explanation of how wonderful AI could become. They need convincing evidence that the revolution will improve their lives enough to justify everything they're being asked to accept.

OpenAI wants to build more enormous data centers to power ChatGPT and its increasingly ambitious AI systems. Unfortunately, Americans are becoming considerably less enthusiastic about having those facilities built in their neighborhoods.

Following a midterm election in which opposition to data centers became a significant political issue, OpenAI promised in a new announcement to be more transparent and listen more carefully to the communities expected to host them.

Under its new community infrastructure commitments, OpenAI says it will provide more information about construction plans and their environmental effects. The company is also moving away from requiring public officials to sign non-disclosure agreements during negotiations, with limited exceptions for sensitive information. It's a notable change for an industry that has often approached local development as though the promise of technological progress should be enough to overcome almost any objection.

The timing of the announcement is notable as it comes after the Nov. 3 U.S. elections, which saw the resounding victory of almost every effort to restrict or forbid new data centers and the defeat of many of their political advocates. Across the country, communities have been questioning the electricity demands of AI infrastructure and whether the economic benefits justify the disruption.

The sweetness-and-light approach differs from the inevitability many AI companies have promoted around the technology. Given the origin of the new message, it wouldn't be surprising if some of the pro-data center efforts were drafted or tested with AI assistance. ChatGPT might be helping persuade Americans to welcome more infrastructure for itself.

Data center resistance

To encourage acceptance, AI companies have couched data centers as essential investments in America's technological future and promised new jobs and tax revenue.

But those living near a proposed site have their own calculations regarding how much energy, water, and other resources the data centers consume. That might mean higher bills or a strain on the local infrastructure that could lead to its failure or necessitate expensive upgrades. The possibility of paying more for electricity so a technology company can operate a profitable AI service isn't an especially compelling sales pitch. And whether the long-term economic benefits will match the promises made during development remains uncertain.

Those concerns have helped turn data center construction into a political issue that crosses traditional party lines. Communities that disagree about plenty of other subjects can find common ground in opposing a massive industrial facility near their homes. The technology industry is discovering that local opposition doesn't disappear simply because a project involves artificial intelligence.

The midterm results have made that lesson harder to ignore. Candidates and local campaigns challenging data center expansion found receptive audiences among voters worried about infrastructure costs and environmental effects.

ChatGPT tries to win over the neighborhood

OpenAI's response promises a more cooperative approach. The company says its new framework will involve communities earlier in the development process and provide clearer information about the consequences of proposed facilities. It also intends to establish local agreements that address concerns and make commitments more transparent. The company even promises to cover all electricity and infrastructure costs for the facilities it owns and operates.

The decision to reduce the use of non-disclosure agreements is probably essential in the current political climate. Suspicious residents are demanding to know what representatives are negotiating on their behalf, particularly when those decisions could affect local finances and infrastructure for years. The company has highlighted local investments tied to existing developments, though that may not convince every community that a massive data center is a good neighbor.

The company's new approach also has the unmistakable feel of a marketing campaign. OpenAI needs more computing capacity, and growing public opposition threatens to make it slower and more expensive to obtain. Americans are becoming increasingly willing to challenge the assumption that every new AI facility represents progress worth welcoming. If OpenAI wants to keep expanding, it will need to offer something more convincing than polished promises, whether a human or a chatbot helped write them.

My handwriting has never been particularly well suited to the digital age. It's perfectly readable to me, at least most of the time, but turning a page of hurried notes into something I'd actually want to share usually involves typing everything out again. Google's latest Gemini upgrade promises to eliminate much of that work, offering to create and edit documents, PDFs, spreadsheets, and more directly inside Gemini.

You can generate a file from a prompt and refine it without repeatedly downloading new versions. Any Gemini user can try it out. Just upload a file to Gemini and ask Gemini to transform it into a document.

You can preview it directly in the conversation, expand it to make manual edits, or highlight a particular section and ask the AI to change it. That means a minor correction no longer has to become a lengthy conversation with multiple regenerations of entire documents.

Sandwich history

For my experiment, I started by taking handwritten notes during a short presentation about the history of sandwiches. The page covered the ancient origins of putting food on bread, the famous story of the fourth Earl of Sandwich, and the role of industrialization in making sandwiches a familiar convenience. It also included the introduction of sliced bread in 1928, a development that presumably made life easier for everyone except people who preferred cutting their own.

The notes were recognizable as the product of someone trying to write faster than the speaker was talking. I photographed the page and uploaded the image to Gemini, asking it to turn the notes into a neat, readable PDF.

Most importantly, the content largely survived the transformation. Gemini retained the story of John Montagu supposedly ordering meat between bread during a card game in 1762, along with the explanation that sandwiches became popular because they were inexpensive and convenient. It also preserved the important distinction that the gambling story is probably exaggerated, rather than presenting the legend as unquestionable historical fact.

It's easy to see the applications for students, who could photograph handwritten lecture notes and produce a study guide, or a meeting attendee who could turn a page of hurried observations into a document others will understand. You'd still need to check that Gemini has interpreted everything correctly, but it removes much of the tedious retyping that would ordinarily follow.

Revamping the doc

The PDF came out alright, but I wanted to see how the editing performed. I asked Gemini to move the line about medieval stale bread to its own section and add some more detail to go with it, but asked Gemini to otherwise keep it the same.

It did pretty well except for deciding to add visible citation markers that didn't actually cite anything. The document was based on my own notes, but Gemini seemed to think it needed more documentation. That could be fixed in a follow-up, but it was a flaw nonetheless.

The second version also simplified some of the original wording. The explanation of industrialization became shorter, while the detail about sliced bread was reduced to a brief mention of 1928. None of those changes ruined the document, but they showed how asking AI to improve something can sometimes cause you to lose details you wanted to keep.

For ordinary users, that ability to keep refining a file within the same conversation is arguably more valuable than generating an impressive first draft. The feature won't eliminate the need to proofread, especially when Gemini interprets handwriting or adds information beyond the original material. It can misunderstand a phrase or introduce unsupported details, and a polished layout can make those mistakes easier to overlook. But combining document creation and editing makes the process feel much more complete. Neat and efficient, much like the dream of the Earl himself.

• Texas Governor-elect wants more protections against AI expansion
• Gina Hinojosa says AI firms are ‘taking advantage of Texas’
• Communities must ‘have a seat at the table’ in AI discussions, Hinojosa says

Texas has long been one of the most data center-friendly locations in the US, with new facilities springing up at a tremendous rate of knots over the last few years. But with mounting public opposition and a so-called “blue tsunami” in the US mid-term elections, that could be about to change, with the state’s incoming Governor-elect warning that there will be a new game in town for the massive AI firms that have outsourced operations to Texas.

Speaking during an interview with CNN’s Jake Tapper, Texas Governor-elect Gina Hinojosa said that any work with AI companies “has to be a partnership that benefits Texans. And right now, what has happened is that data centers have moved into mostly rural communities and there are no rules and laws to protect Texans. And Texans are being taken advantage of.”

Hinojosa promised to introduce laws to reverse that situation in the state’s dealings with AI outfits, adding that “we need to stop what we are doing with data centers until we have those protections.” One of those changes, Hinojosa suggested, was to ensure that “communities have a seat at the table to decide if and how these data centers come into their communities.”

“We need to make sure they are partners in this effort,” she continued. “ It is not that I hate data centers. I hate the way they are taking advantage of Texas, and that stops when I’m Governor.”

Data center free-for-all

Hinojosa campaigned on the promise of a temporary data center moratorium in Texas. Her stance seems popular with residents of the Lone Star State, with a CNN poll showing that 75% of respondents in Texas opposed building data centers near where they lived.

Hinojosa is far from alone in her opposition to the expansion of data centers throughout the US, and many Democratic candidates who were victorious in the US mid-term elections campaigned on similar opposition to the facilities — as did some Republicans.

This could raise questions about AI companies’ future plans, with several of the biggest names previously planning to build data centers that could face stiff opposition from elected officials. For example, Anthropic noted in its recent IPO filings that the supply chain for its data center infrastructure is highly complex and any “delays or failures” along the way could significantly impact the company.

It’s not entirely clear what shape Hinojosa’s new approach to data centers could take. But from her statements so far, it looks like the expansion free-for-all that has affected Texas — and many other communities across the US — could be coming to an end.

• Famed game creator Hideo Kojima has voiced his opinions on AI
• He said human creativity is a vital part of the game-development process
• And he’s worried about a future where AI dominates the industry

Legendary games maker Hideo Kojima is known for the creativity of his output, from the Metal Gear Solid series to Death Stranding, so it’s unsurprising to hear that he has strong opinions on the use of AI in video games. And in a fresh interview, he told readers exactly where he feels AI falls flat compared to human-made artistic expression.

Speaking to Japanese site Game*Spark, Kojima said that while it’s good that AI has improved efficiency, “the fun isn’t in finishing the picture. It’s in the process … While it’s fine to tell a computer to create this or that, and then say, ‘That’s wrong, change it,’ I don’t find that very interesting in terms of craftsmanship.”

“I’m worried about what will happen to that aspect of the enjoyment of craftsmanship in the future,” he added.

Kojima said he feels that there’s a distinction between using AI for creative output and employing it to form a finished product: “If you’re just making a ‘product,’ that’s fine, but I think the fun for creators lies in the creative process itself. I think ‘creativity’ and ‘products’ are different things.”

In other words, AI doesn’t offer creators the kind of spark they get from crafting something themselves. And it’s that kind of magic that so often resonates with consumers of Kojima’s media.

His comments reflect those of Jason Graves, the composer of Call of Duty: Modern Warfare 4’s soundtrack, who spoke out against AI music generation, saying that “Music is not just a bunch of notes strung together.” Instead, the process is essential as it is based on “creative instincts forged by emotion and all the little decisions that give a piece of art its own unique personality.”

The effort itself is key

Kojima didn’t just muse on what AI means for video games and other creative outlets in the present — he also had thoughts on how it could impact the future.

For instance, the game director said: “It’s not about telling someone to make a song like this and then being satisfied with the result; it’s the trial and error in between that gets the adrenaline pumping, and it’s incredibly exciting. I don’t want a future where that’s lost.”

He then reflected that, “I think it will be fine for five or 10 years, but if AI continues unchecked, perhaps a future where AI plays a vital role in creative fields will come someday. Well, I won’t be around by then. It’s up to the younger generation to decide, so I want to pass the baton to them.”

If the goal is purely to improve efficiency, AI could help to build a “good future,” Kojima noted. But for someone like him, there’s evidently much more to crafting something than just making it quickly with minimal effort.

Indeed, the effort itself is a key part of creative development. As Kojima put it, “Even with plastic models, the shape is the same no matter who builds it, but the process of assembling it is what’s interesting, isn’t it?”

For years, the retail industry treated the touchscreen as shorthand for interactivity. If shoppers could browse an endless aisle, customize a product or trigger an animation, the store was considered interactive.

That definition is beginning to look dated. The next generation of interactive stores will not be defined by what shoppers touch, but by what the environment understands — and how appropriately it responds.

This is an important distinction. A store can be filled with digital displays, interactive kiosks, sensors and immersive installations and still behave like a static space. The content may be visually impressive, yet remain unchanged regardless of what is happening on the sales floor.

The same promotion runs when inventory is plentiful or nearly depleted. The same message appears during a quiet morning and a crowded evening. Every screen is managed as a separate endpoint rather than as part of a coordinated customer experience.

That is technology added to a store, not a truly interactive store.

Digital channels

Digital channels have already established a different expectation. Websites and apps routinely adapt to location, browsing behavior, product availability and the stage of the purchasing journey. Yet many physical stores continue to communicate in exactly the same way throughout the day, even though conditions inside them constantly change.

Shoppers do not describe this disconnect in technical terms. They simply notice the difference between an environment that helps them and one that keeps broadcasting messages at them.

A genuinely responsive store adapts communication to context. When foot traffic increases in one area, nearby displays can prioritize information relevant to that location. When an item is running low, promotional content can be adjusted instead of continuing to create demand the store cannot fulfill.

When a line begins to form, screens can provide useful updates, redirect customers or reduce the perceived wait. When a campaign performs better at certain times or in particular stores, retailers can optimize it without rebuilding the entire content plan.

The objective is not to surround shoppers with more stimuli. It is to make every interaction more relevant.

Three forces

Three forces are moving physical retail in this direction: operational data, artificial intelligence and retail media.

Operational data allows the store to become aware of its own conditions. Foot traffic, dwell time, inventory, queue length, daypart, weather and activity by zone can all help determine what information is most useful at a particular moment. Importantly, many of these applications do not require identifying an individual shopper. A retailer can make the environment more responsive using aggregated or operational signals, with clear privacy rules and appropriate governance.

AI can help interpret those signals and turn them into decisions: which content to prioritize, when to activate or pause a campaign, how to adapt a message and where automation can reduce repetitive work. But the goal should not be to hand every decision to an algorithm. Retailers need business rules, human oversight and clear limits defining what the system may change. AI is most valuable when it supports operational judgment rather than becoming another opaque layer of technology.

Retail media adds a third dimension. In-store screens are evolving from communication tools into media inventory that can generate revenue close to the point of purchase. That opportunity is significant, but it also creates competition for attention. A paid campaign, a store promotion, a wayfinding message and an operational alert may all need the same screen at the same time.

This is why the next stage of store technology is not primarily about managing more screens. It is about orchestrating communication.

Shared logic

Retailers need a shared logic that decides which message takes priority, under which conditions and for how long. That logic must work as consistently in 500 stores as it does in a flagship. Otherwise, every new data source, media campaign or interactive feature creates additional manual work for local teams and another potential point of failure.

Complexity is one of the biggest risks in retail technology. Innovation is too often measured by accumulation: more displays, more systems, more dashboards, more vendors and more data. The result can be the opposite of what was intended — stores that are harder to operate, employees who are asked to manage tools outside their core responsibilities and experiences that cannot be scaled beyond a showcase location.

Technology only creates value when it simplifies complexity for the people running the store. That does not mean pretending the complexity has disappeared or placing an “AI-powered” label over it. Real simplification requires doing the difficult work behind the scenes — connecting data, setting priorities, integrating systems and establishing governance — so that store teams receive only what they need. The ideal outcome is not another dashboard. It is a screen that does the right thing at the right moment without demanding constant intervention from a store manager.

The same principle applies to creativity. Good automation does not replace creative thinking; it gives it room to scale. It allows a strong concept to move beyond a one-off installation and become an experience that can be deployed, measured and improved across an entire store network.

The future of interactive retail

That is why the future of interactive retail will not be built around isolated spectacle. The “wow” effect can attract attention, but it does not necessarily create lasting value. An installation that impresses shoppers for 10 seconds but cannot be updated, maintained, measured or integrated into daily operations has a limited future.

The next phase may look less overtly technological, but it will be more consequential. The best stores will not necessarily have the most screens or the most visible technology. They will feel more intuitive. Content will appear when it is useful. Displays will help organize the experience instead of competing for attention. Communication will reflect local conditions. Store operations will become more fluid. Shoppers will sense that the environment responds better to their needs, even if they cannot see the systems making that possible.

The challenge for physical retail is no longer simply to digitize the store. It is to give the store the same ability to adapt that customers already expect from digital channels.

The interactive store of the future will not be the one with the most touchscreens. It will be the one that listens to what is happening and responds at the right moment.

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Sam Altman spends much of his time explaining how AI could transform the world for the better and pushing back against doomsayers. Nonetheless, the OpenAI CEO has some fairly unsettling ideas about how AI development could lead to a disaster, or disasters plural. He outlined a couple of these nightmares during an episode of the Figuring Out With Raj Shamani podcast in disturbing detail.

The two dystopias stem from almost opposite problems: either humanity losing control of increasingly powerful machines, or one group seizing control of most of the technology.

"One is that there’s a loss of control to AI, and we intentionally or accidentally sort of cede control of the future to systems that are unbelievably more capable than we are, and we can’t get it back," Altman said. "And the other is like, one person or company or country gets way too powerful relative to everybody else."

The first scenario is less the tired cliché of a robot uprising and more of a soft surrender of humanity's choices to AI until it's impossible to take back any agency from the AI software. The other is a more modern, if still clichéd, speculative-fiction scenario: an overwhelmingly powerful technology controlled by one person or corporation, leaving everyone else at their mercy. They are both endlessly discussed by people in the industry and policymakers, but having Altman issue the warning is still notable.

Altman, through OpenAI and ChatGPT, is one of, if not the central face of AI to those who have heard the bare minimum about the technology and its flaws. Altman believes those capabilities could bring extraordinary benefits, but his comments suggest he's not sanguine about the looming chaos that AI carries as well.

Handing over decisions

Advanced AI taking over because people cede control has only become a real-world concern in recent years as the technology moved beyond simply answering questions and into decision-making territory. AI agents can already interact with websites and complete complex assignments, and developers are working toward systems that require less and less human supervision. The more responsibility those systems receive, the greater the consequences if their objectives diverge from what people actually intended.

It's important to remember that what we call AI now relies on probability prediction in different forms. While very powerful and leveraging huge amounts of data, it can't actually make judgments like a human, and doesn't have to be malicious to be harmful. That's why models tend to opt for the disastrous choice of using nuclear weapons as a strategy when simulating how to run a nation.

Humans might not understand or approve of how an AI pursues an objective, even if humans issued the task. And if the AI model has enough power and is embedded in enough critical systems, people might not be able to redirect or stop it. After all, an AI system told to reduce gridlock on the road might logically conclude the solution is to remove the roads, or send an order to any Wi-Fi-enabled car to stop operating. That particular scenario is very unlikely, but the fact that it's not actually impossible is unnerving enough.

We're all familiar with the more human problem of technological and industrial monopolies Altman extended to AI in his other apocalyptic warning. Plenty of people have sounded the alarm about how there is already too much power in too few hands with too little regulation. Altman's hands being among them doesn't mean he won't criticize the fact.

"I think it’s actually much better for the world for there to be multiple AI efforts with slightly different approaches that are competing with each other and putting out different ways," Altman said. "I also have this like dislike of too intense power concentration. I think that’s not what works, so I’m hopeful we don’t do that.”

Altman's disdain is in some tension with his company's ambitions and actions. Claiming OpenAI is already dominant in several respects, such as AI infrastructure, is an illustration of the dominance he says he worries about.

"I think we have the most users and the best stack from chips to data centers to models to other things that come around that sort of put abundant intelligence into people’s hands. I think we have the best brand and the values that people who are going to use this technology want," Altman said. Nonetheless, he claimed, "we want everybody else to succeed, and I think people can feel that from us.”

Optimistic doomsaying

Despite those warnings, Altman remains fundamentally optimistic about where AI is taking humanity. Asked about his children growing up in a world increasingly shaped by the technology, he expressed confidence that people would find ways to adapt. His outlook suggests that he sees AI's risks as serious challenges to manage rather than reasons to abandon its development.

"I don’t know what the future’s going to look like, but I am comfortable that I have enormous trust in people to figure out how to adapt and thrive and make society better for all of us," Altman said. "I think the world will most likely keep getting much better."

That optimism extends to OpenAI's future plans to apply AI to scientific exploration. He pointed to recent mathematical achievements as evidence that the technology might be approaching a point where it can contribute meaningfully to fundamental research.

"I am extremely excited to see how far we can push AI to discover new science," Altman said. He envisions "a machine that can help us understand the world better, discover the deepest insights in physics, figure out new medical treatments, a new source of energy, things that I think we can’t even imagine but that better scientific understanding will enable. I am extremely excited for us to go build that machine."

• Sam Altman says he wants there to be enough RAM for everyone
• He argues his past comments were taken out of context
• Altman previously criticized people using RAM sticks in their home PCs

When OpenAI chief Sam Altman said that it was “quite terrible” that everyday folks were using RAM sticks in their computers when there wasn’t enough for all the data centers his company wants to build, it went down just as badly as you’d expect. Now, Altman has tried to walk back his comments and say it was all just a misunderstanding.

For full context, Altman’s original remarks were made on the Cheeky Pint podcast, where he said: “From a global allocation perspective, actually, it’s quite terrible to have everybody have a bunch of the world’s limited supply of DRAM sitting in their own computer… You want maximum utilization in the cloud if you’re trying to deal with this very supply constrained environment. And somehow many people’s intuition is the opposite of that.”

Posting on X, Altman agreed to a user’s request to change his name to “Ram Altman” in reference to his earlier statements. Altman then commented that he would make the name change “but only because I actually do want the world to have tons of memory and people to put as much as they want in their computers, and it is f*cking annoying that this was taken out of context. I have been arguing for the world to ramp up memory production for a long time.”

Replying to a different X user who asked “why u want my RAM then,” Altman stated: “I want you to have so much RAM.”

A problem of OpenAI’s own making

Some readers might find Altman’s claim that his quotes from the Cheeky Pint podcast were taken out of context to be somewhat disingenuous. As can be seen in the interview transcript, Altman was speaking about people running local AI models on their computers who might feel that these models are the future and that data centers are unnecessary. It was then that his quote starting “From a global allocation perspective” began.

It’s therefore hard to see his comments as being anything other than a criticism of people having memory sticks in their home computers, because it's bad for data centers.

Part of the problem is that Altman’s own company is one of the foremost reasons why RAM stocks are severely depleted in the first place. The global rush for memory is almost entirely due to the needs of AI data centers, leaving everyday consumers with next to nothing.

Altman’s solution — “I have been arguing for the world to ramp up memory production for a long time” — also feels like an underwhelming answer to the problem, as it will take time for new fabrication plants to come online, with some analysts and firms predicting that the current shortages could continue for years.

Altman’s latest comments will therefore probably do little to endear him in the eyes of PC users — despite his protestations.

OpenAI is preparing to combine the number of subscribers who can use ChatGPT and its Codex coding assistant. Instead of separate measurements, Plus, Pro, and Business subscribers will have a single gauge for both, according to the company's updated documentation. More complicated tasks will consume more of that allowance, although ordinary Instant conversations won't count against it.

The change comes as OpenAI prepares to bring ChatGPT Work's more autonomous capabilities into the main ChatGPT experience. Users will be able to ask ChatGPT to carry out complicated projects directly from a conversation. That convenience also means combining the resources available for their use, and I suspect plenty of paying customers are going to be unhappy about that particular trade-off.

OpenAI says most subscribers shouldn't find themselves waiting for their usage limits to reset more often than they do today. The company hasn't disclosed exactly how large the new allowances will be, making that reassurance rather difficult to evaluate. It's asking customers to trust that the subscription they're already paying for won't become less useful once the rules change.

OpenAI has made it clear it wants ChatGPT to become a comprehensive platform for both ordinary consumers and people using AI to run substantial professional projects. I can see the appeal of putting everything on one platform, but I think the company is underestimating how irritated people become when a familiar service starts reorganizing itself around customers with very different needs.

One subscription, too many expectations

For most people, ChatGPT is still a relatively straightforward assistant. They probably aren't especially interested in how many tokens a software development agent consumes while building an application. That makes the new shared allowance awkward. A person who uses ChatGPT for research and occasionally experiments with Codex could find those activities competing for the same resources.

Instant chat remaining outside the allowance is an important protection, and it means ordinary conversations shouldn't suddenly become impossible because someone used Codex too enthusiastically. But more demanding reasoning tasks will count, and those are precisely the capabilities that help justify paying for a subscription.

The eventual allowance could be generous enough that most people barely notice the change, and some users might benefit from spending more of their resources on whichever tool they prefer. But without clear figures, customers have no meaningful way to determine whether they're getting a better deal.

The reaction among existing users suggests OpenAI has already stumbled over that problem. "Worst decision ever by OpenAI" is the header and general sentiment on one Reddit thread. "Def unsubscribing for this one, it was the main appeal to have a 20$ sub next to the Claude's 200," one user wrote.

The anger reflects a broader worry that OpenAI is increasingly prioritizing professional AI tools at the expense of people who simply want to use ChatGPT without worrying about how much computing power they've consumed.

The frustration is understandable, particularly given OpenAI's recent adjustments to its Pro plans. The company reversed a proposed reduction in Pro 200 usage limits after subscribers complained, while also increasing allowances for other plans. That's welcome responsiveness, but it hardly inspires confidence when another substantial change to usage accounting is already approaching.

Business first

The more interesting question is what OpenAI ultimately wants ChatGPT to become. Its increasingly ambitious Work capabilities are designed to carry out substantial assignments, moving the product closer to a professional productivity platform. That's a big departure from the friendly chatbot that attracted millions by making AI accessible through ordinary conversation.

OpenAI seems determined to combine two rather different experiences, and the danger is that ordinary users end up navigating a service increasingly designed around professional workflows.

The company has good commercial reasons to pursue this direction. Businesses are willing to pay substantial amounts for AI that can perform valuable work, while consumer subscriptions face much greater pressure to remain affordable. Combining the experiences could encourage people to experiment with advanced capabilities, potentially making more expensive subscriptions attractive.

But if I pay for access to advanced reasoning, I'd like to know roughly how much I can use it without wondering whether another task has eaten into my allowance. Having to monitor token consumption feels like the sort of administrative chore AI was supposed to eliminate.

OpenAI should publish understandable allowances and explain how different activities affect them before the December changes arrive. Customers shouldn't need to conduct their own experiments to determine whether their subscriptions have quietly become less generous.

Making ChatGPT more powerful is worthwhile, but making people feel they're getting less for their money would be an exceptionally foolish way to celebrate that progress.

Elon Musk wanted to prove that AI could make a better adaptation of Homer's The Odyssey than Christopher Nolan. After months of complaining about Nolan's film, he promised that Grok Imagine would produce a more faithful version of the ancient Greek epic. The first seven minutes have debuted, effectively arguing for leaving filmmaking to people who actually care about telling stories, not pursuing petty bigotries.

The project also has an ugly origin that's worth remembering. Musk's objections to Nolan's adaptation centered heavily on its casting, particularly Black actress Lupita Nyong'o and transgender actor Elliot Page. He accused Nolan of sacrificing historical accuracy to satisfy Hollywood sensibilities, and described the director's casting choices in explicitly racist terms. Ignoring the historical and textual incoherence of his arguments, he promised a full-length AI-generated alternative.

The seven-minute opening episode, produced using Grok Imagine in collaboration with Wonder Studios and director Dev Walker, showcases an astonishing marriage of bad ideas and poor execution, backed by bottomless resources and prejudice.

7 minutes wandering the uncanny valley

The one positive comment that doesn't ring false is that some of the landscapes look nice and atmospheric, and some shots of ships sailing across the water could pass for outdated video game cutscenes that would have been impressive at the time. Otherwise, Grok's Odyssey resembles a collection of trailer clips from someone who watched Gladiator half-asleep 20 years ago and has looked at the covers of some Greek mythology books.

The dead-eyed Odysseus sounds like someone trying to pretend they are angry the whole time, even when he's talking to the family he supposedly loves. The events that should establish the story's emotional foundation have less emotional depth than a pop-up book.

A human filmmaker and actor could turn Odysseus's departure from his wife Penelope into a moment that haunts the whole film. Grok simply presents the event and moves along, apparently satisfied that the relevant plot point has been included.

That's what makes the comparison with Nolan so embarrassing. Whatever anyone thinks of his films, Nolan understands that cinema depends on controlling an audience's attention and emotions. His movies are constructed around deliberate creative decisions, with performances and editing working together to give individual moments significance. Grok can imitate the appearance of those moments, but the imitation has little weight.

Grok's not winning an Oscar

AI keeps getting better, and future models may eliminate many of the visual inconsistencies that currently make AI footage so recognizable. But technical improvement alone won't give a machine the personal experiences or creative intentions that shape meaningful art.

A great film reflects the choices of people who have something they want to communicate. Directors bring their own understanding of the world to a story, while actors draw on human experience to make fictional emotions believable. The best scenes work because somebody understands exactly what the audience should feel and has carefully shaped the experience to produce that response.

That said, a talented human director might use AI to enhance or augment a scene that would otherwise be impossible on a limited budget. But any artistic value would come from the filmmaker's decisions.

But regardless of the intent of Wonder Studios and its team, Grok's Odyssey looks terrible, and the blatant bigotry fueling Musk's comments would make for queasy viewing even if it were somehow comparable to Nolan's epic. Producing seven dull minutes for a fraction of a Hollywood budget doesn't automatically make those minutes an artistic achievement.

The Odyssey and all of its best interpretations have endured partly for what they impart about the very human experience of longing for our loved ones. Those emotions have kept audiences interested for thousands of years. Grok has managed to reproduce some of the costumes and scenery while making the characters feel almost entirely irrelevant.

The AI Odyssey has become a seven-minute reminder of why human creativity matters. For all the technology involved, the most revealing thing about this experiment is how little artistic imagination it contains.

• Grok Bot users recently found they were hitting their usage limits very fast
• That’s because Elon Musk decided to throttle Grok Bot usage limits
• The X owner said Grok Bot costs were "unsustainably high"

If you noticed that you burned through your Grok Bot usage much faster than usual in recent days, there’s a reason for that: Elon Musk intentionally limited users’ AI agent allowances in the face of “unsustainably high costs.” And as you’d expect, a lot of people are unhappy with that.

The revelation came about after an X user pinged platform owner Musk, asking for a Grok Bot reset after they surprisingly hit their 100% usage limit, four days before it was due to reset. They additionally commented that “Normally I don’t even reach 100%.”

Musk then replied to the post, stating: “The cost of serving Grok Bot was unsustainably high relative to revenue.”

Many X users seemed irked by the situation, with several saying that their Grok Bot usage had hit the 100% limit while they were sleeping and not using any AI agents. Users claimed their usage had suddenly gone from 50% to 100%, 35% to 78%, and 70% to 100%, all supposedly without using Grok Bot at all.

Unsustainable costs

Elon Musk’s comments come amid broader scrutiny of the financial sustainability of many AI products. Tech stocks — including those of many leading AI firms — fell sharply last week as investors grew concerned over the profitability of the industry, with Google revealing that its own AI summaries have hurt its search advertising revenue.

Concerns have also been raised in recent months over the finances involved in many AI companies’ offerings. For instance, users of AI tools like ChatGPT and Claude Code can often spend many times more in tokens than they’re actually paying in monthly subscriptions, leading to questions over how sustainable many AI business models really are and whether things will need to be adjusted in the near future.

This changing landscape hasn’t gone down well with many users, of course. One thread on Reddit dubbed Musk’s about-face an “indirect rug pull” of Grok Bot, with features and usage limited in a bid to cut costs.

But it’s unlikely that AI producers will be able to subsidize their products’ costs to users forever. Usage limits will necessarily need to tighten (as OpenAI quietly announced recently, much to the frustration of users) and prices will need to increase for these business models to make sense. So don’t be surprised if Grok Bot’s lower cap is just the first of many big changes looming on the horizon.

Google has unveiled an AI model that can create entire interactive worlds, complete with realistic environments that respond to what people do inside them. Genie 4 is an extraordinary piece of technology, capable of generating experiences that would have seemed almost impossible just a few years ago. Unfortunately, Google seems incapable of announcing an impressive AI achievement without dragging artificial general intelligence (AGI) into the conversation.

"The ability to explore an unlimited range of realistic environments is a key stepping stone on the path to AGI," the company declared in its rollout. "Training and testing AI agents in truer-to-life environments brings us closer to their successful deployment in the physical world."

Despite the science behind that claim, I'm skeptical — or perhaps cynical. Though I pay closer attention than the average person, I've lost count of how many times I've heard that the latest AI demonstration, no matter how impressive or lackluster, is a sign that AGI is right around the corner.

Genie 4 produces more realistic scenes than Genie 3 and includes audio, even dialogue that responds to what happens inside the simulation. Users can even steer the experience through conversational instructions, changing the environment while the generated world continues around them.

The possibilities for creative experimentation and AI research are intriguing. But the distance between generating a convincing virtual environment and developing genuine general intelligence remains enormous.

Pretty but nothing long-term

Genie 4 offers striking, vivid environments. You can imagine walking through a city, changing the weather, and interacting with simulated inhabitants. That's not nothing, and it's a huge leap from even a few years ago. But it would be foolish to confuse realism with the understanding implied by AGI.

A model can produce a visually convincing simulation of a ball rolling down a hill without knowing any physics. Faking plausibility is easier than grasping the underlying rules that govern how things behave in reality, especially when a user does something unexpected.

And Genie 4 can maintain its coherent environments for only a few minutes, with visual details drifting over time and objects abruptly changing or vanishing.

That's a fine limitation to work with, but it doesn't scream universal intelligence that understands the real world. If a simulated environment struggles to remember what happened a couple of minutes earlier, trusting it to model the consequences of complicated real-world decisions seems premature at best. The scenery might look real for a minute, but the underlying reality remains fragile.

None of that makes Genie 4 a failure or its capabilities uninteresting. But the AGI framing encourages you to read much more into its abilities than the evidence supports.

AGI as marketing

Google has a serious reason to pursue world models like Genie 4. It could help teach AI agents and robots how actions affect their surroundings, and simulations could provide training opportunities that would be expensive or dangerous to recreate physically.

A simulated world is only useful for training when its behavior corresponds closely enough to reality. If an AI learns from inaccurate physics or unpredictable interactions, it risks becoming very good at solving problems that don't exist outside the simulation. Genie 4 could assist in reducing that problem. But demonstrating a promising research approach is far from showing that the approach will produce AGI.

Not that anyone would agree on whether AGI had been achieved even if it did happen. There's no official definition for the concept. Companies can describe progress toward AGI without explaining exactly how much progress they've made or what evidence would prove they've arrived. The phrase has become a wonderfully flexible destination, always close enough to sound exciting and distant enough to avoid being measured.

I suspect Genie 4's most immediate value will have little to do with creating a machine that thinks like a person. Its ability to rapidly produce interactive prototypes could be useful for game developers, while researchers could use controlled simulations to explore particular problems. Those are worthwhile achievements that deserve attention without being wrapped in promises about the future of intelligence.

What frustrates me is how speculation around AGI reduces the new model's milestones to a slogan. It turns an interesting scientific development into another installment of an increasingly familiar marketing campaign.

For now, Genie 4 shows that Google is getting much better at creating worlds that look real. That's an achievement worth celebrating on its own terms. We should resist the temptation to confuse the ability to generate a convincing imitation of reality with the intelligence required to understand it. And try not to count any more AGI chickens before their models hatch.

• OpenAI will keep pushing AI’s scientific discoveries, Sam Altman says
• Some researchers are excited by the ‘fantastic’ breakthroughs
• But others warn that the pace of change is ‘psychologically taxing’

One of the promises that huge AI companies like OpenAI and Anthropic have made is that their machine learning models will help to solve some of the oldest problems in math and science. The pace of their discoveries has left many experts struggling to keep up, but OpenAI’s Sam Altman just has just said there’s much more in store — and not everyone is happy about that.

Speaking on the Cheeky Pint podcast, Altman outlined his belief that “Humanity deserves fundamental knowledge, and we should not be sitting on a major fundamental science discovery.”

That rationale is why OpenAI has published breakthrough after breakthrough in these fields, knocking down some of the oldest and toughest problems that have troubled great thinkers for years. But the way in which the company has set out its results has drawn criticism for often inadequate attributions and questionable verification processes.

While Altman acknowledged that, saying on the podcast that “I do have empathy for the method” and saying OpenAI’s quest for mathematical challenges did “get out of control” at times, he suggested that there’s much more to come. Asked if the firm has anymore unpublished breakthroughs in its vaults, he replied “We do. Of course.”

That’s led to accusations that, far from giving people “fundamental knowledge,” OpenAI is gatekeeping important discoveries. And there’s another problem: the sheer scale of the new research is swamping academics. For instance, Wired has noted that the company recently “dumped 722 manuscripts on 400 problems on GitHub,” leaving the scientific community to pick through the enormous mass of information.

And the impact of having the most important work in your life suddenly undercut by a machine is “psychologically taxing for many people,” said Thore Husfeldt of the IT University of Copenhagen. Husfeldt added that there is “a lot of doom and gloom, because every week, or every second week, the results announced by AI just annihilate the ongoing research projects of many people.”

Obliterating entire fields of research

AI models’ ability to solve thorny problems was keenly demonstrated when it apparently disproved the exponential time hypothesis (ETH), which argued that the effort needed to solve a problem increases with its complexity. That’s upended countless theories that assumed that ETH was correct and left many experts scrambling to catch up.

As Linköping University professor Peter Jonsson told Wired, although AI’s tendency to undermine years-long research can be irksome, “at the same time, we learn a lot from it. And this is what science is about.”

UC Santa Barbara professor Daniel Lokshtanov aptly described the mood among many scientists and mathematicians, saying “It’s just a giant bomb dropped into the middle of the field,” he says. “It’s really cool and terrible at the same time.”

For Husfeldt, meanwhile, the discovery is “fantastic.” He added: “Being a mathematician is a lot like stumbling about in a completely dark room. It’s like somebody just illuminated an entire subfield in floodlights of knowledge, and now we just run around like overexcited bunnies and look at all the nooks and crannies that we can suddenly explore. So it’s a super exciting time.”

In the case of the ETH theory, OpenAI’s work has not been peer reviewed or independently verified. But it’s clear that AI research is creating generational changes in many areas — and overturning entire fields of study in an instant, with all the excitement and chaos that comes with that.

Jonsson fittingly summed the situation up, saying: “I don’t care if it’s a human-written proof, or it’s written by AI, or if an alien transmitted this proof to us. If it’s true, then we must simply use it in the future.”

• Google's subsidiary Tuike Finland cleared and drained 330 hectares of forest at Muhos for a data center site
• The company failed to get the necessary environmental impact assessments completed before carrying out the felling
• A preliminary police investigation is now taking place, based on 'ambiguities and ‌shortcomings in the drainage and water management', although the results of this won't be known until 2027

A preliminary police investigation is now underway into Google's clearing of a forest in Finland to make space for an AI data center.

As The Guardian reports, the allegation is that Google's subsidiary Tuike Finland cleared and drained 330 hectares of forest at the site in Muhos before it had ensured the necessary environmental impact assessments were completed.

Yle (Finland's public broadcasting company) further reports that the police have said that the investigation is into suspected environmental damage, but the other possible charges could be added depending on its ongoing findings.

Finnish police said: "Based on preliminary information, there have been ambiguities and ‌shortcomings in the drainage and water management at the construction site of Tuike Finland ​Oy's datacentre project."

The investigation will be far from a short process, though, and won't be completed until some point in 2027.

Construction work was stopped on the data center in Muhos, and also at a second data center site at Kajaani-Otanmäki, on October 14. In both cases, the environmental assessments were apparently incomplete.

Antti Järvinen, who heads up Google Finland as country manager, apologized for the forest clearances, but said that Google had acted according to the "best understanding we had of the legislation at the time."

Analysis: the rush for AI data centers

Some 400,000 trees have reportedly been felled across both sites (250,000 of those at Muhos). As The Guardian makes clear in a separate report from last week (via Tom's Hardware), there are a lot of very unhappy Finnish people, as you might imagine.

Many of them question the building of data centers at all, and the AI gold rush that's the driving force behind this activity. There's apparently little direct benefit for the people of the country where these projects are underway, with only a small number of local jobs created. As a professor of computer science at the University of Helsinki, Jussi Kangasharju points out, "Really, for Finland, there are no benefits. They are international companies serving international customers. Datacentres ultimately have little benefit for others except the datacentre operator."

Others obviously worry about the impact on wildlife. Finnish conservationists inform us that the three-toed woodpeckers in the forest have been displaced, and The Guardian highlighted that in Otanmäki, wolves that had nowhere to go when the trees were felled ended up coming into a local village.

It will be some time before the criminal investigation is completed, as noted, but you can bet that the results are going to be closely watched.

• A Kentucky man has won a religious exemption against using AI
• The ruling says he no longer has to use AI tools at work
• The man argued AI contravened his deeply held religious beliefs

If you’ve ever looked to the heavens in exasperation after being told you have to use AI at work, your prayers might soon be answered, if a Kentucky man’s example is anything to go by. That’s because he’s just been granted a “first of its kind” religious exemption from using AI after successfully arguing that it went against his beliefs.

As reported by the Religion News Service (via TomsHardware), University of Kentucky research administrator David Durman reacted with apprehension when his employer started asking its workers to use AI in their day-to-day jobs. The problem for Durman was that this would mean offloading his thinking to a machine — something which, as a humanist, went against his principles.

“The process and work involved in thinking through situations, through my work, through my ethics, through the way I handle other people, is a key component in being able to develop myself as a moral person,” Durman told RNS. “And offloading that labor, regardless of whether it’s taxing or not, is undercutting my ability to be a moral actor.”

For Durman, AI also has the potential to harm society. He stated that he advocates for and believes in human flourishing, but that “At a societal level, I think that the use of AI technology runs counter to that.”

Your belief must be sincere

Durman won his religious exemption with the help of the American Humanist Association (AHA), with AHA attorney Alexandria Huiskens saying that “This is the first of its kind at the AHA and for humanism generally to exempt those who see AI use as flying in the face of their freedom of conscience.”

Durman and the AHA based their argument on Title VII, a US federal law that grants workers exemptions in their work requirements due to religious beliefs. Although humanism — which places an emphasis on human virtue and agency — is generally considered to be non-religious, federal courts have ruled that it can be treated as a religion.

The University of Kentucky accepted Durman’s appeal, meaning he no longer has to use AI at his place of work. He does still have to meet and cooperate with his colleagues who use AI, however.

If you think this might be a quick ticket out of using AI, though, there’s a catch. Douglas Laycock, an emeritus law professor at the University of Virginia and the University of Texas, noted that your professed beliefs have to be sincere to qualify under Title VII, so you can’t just pick a religion and say that using AI violates its tenets if you don’t actually subscribe to that belief system.

Still, for those who do hold sincere religious principles and feel that AI contravenes them, there could be a new way to dodge those mandatory AI workflows whenever you head into the office.

Elvis Presley has been dead for nearly 50 years, but apparently even the 'King of Rock and Roll' can't escape the AI industry's appetite for training data. The owners of 274 Elvis songs made famous by Presley have sued Anthropic, accusing the company behind Claude of using his music without permission to build its AI models, and are seeking tens of millions of dollars in damages.

Anthropic disputes the allegations and says it intends to defend the case. AI companies have long argued that training models on copyrighted works can qualify as fair use, an issue courts are still working through. There's an important legal difference between learning patterns from existing material and reproducing protected expression, but that distinction has become a remarkably convenient place for the AI industry to hide its enormous dependence on other people's creativity.

It's hard to sympathize with the companies on the receiving end of these sorts of lawsuits, even when the plaintiffs are not without resources of their own. Unfortunately, these disputes are becoming all too normal. AI-powered music is becoming more widespread, if not necessarily popular, and we've reached a point where discovering that an AI company allegedly used hundreds of copyrighted songs without permission barely qualifies as surprising news.

The only unusual part is that this time the person whose music is at the center of the dispute happens to be Elvis Presley.

A little less uncompensated song use

The lawsuit makes an accusation that goes beyond simply using copyrighted material for training. According to the complaint, Claude could generate material that closely resembled existing Elvis songs, even after Anthropic introduced safeguards intended to prevent infringement. Requests to recreate songs for modern audiences allegedly produced versions retaining recognizable elements of the originals.

An AI model learning about music isn't automatically infringing copyright, and copyright law doesn't give anyone ownership of an entire musical style. But if a commercial AI system reproduces protected lyrics or creates unauthorized adaptations, it's hard to see why the company behind it should get a special exemption simply because the copying happens through complicated software.

AI companies have been trying out various arguments in court, but a lot of it feels like an attempt to claim that AI development is so important that the usual rules should somehow bend around it. Training requires enormous amounts of information, and licensing everything would be expensive or inconvenient, but that's a standard headache for companies whose products rely on copyrighted material.

TV shows and movies license music, whether an original track or just the right to record a new version for their project. No company is allowed to simply grab hold of copyrighted material simply because clearing it would slow down development. AI companies should have to take copyright seriously too, even if that makes their business models more expensive.

It's also particularly galling that songs people spent years creating and popularizing can become raw material for commercial technology without the rights holders having any meaningful say. Access to a work and permission to exploit it are separate questions, regardless of how sophisticated the software becomes. Ignoring those rules is why artists who passed away decades ago seem to produce new music whose earnings don't go to their estate.

AI music turns the volume up

Elvis is hardly the first musician to become part of this fight. Major music publishers have already sued Anthropic over allegations involving copyrighted lyrics, while AI music generator Suno has faced legal challenges from record labels. The Elvis lawsuit stands out because it focuses on the catalog associated with one of the most recognizable performers in history.

That fame might help draw attention to a problem that has affected considerably less powerful creators for years. Independent artists may struggle to establish whether their work was used to train a model. If someone with Elvis Presley's commercial legacy needs a federal lawsuit to challenge alleged unauthorized copying, ordinary musicians' prospects look bleak.

Of course, the companies controlling Elvis's catalog aren't struggling artists themselves, but that doesn't make the underlying complaint any less important. The principle shouldn't depend on whether the copyright owner is wealthy or famous.

I'm also not convinced that every AI company needs unrestricted access to practically everything ever written or recorded to produce useful technology. Claude is an impressive assistant, but its ability to answer questions doesn't suddenly make songwriters' copyright interests irrelevant. If AI developers want to build profitable products using creative works, they should be prepared to explain where that material came from and why they were entitled to use it.

The courts may decide in Anthropic's favor due to fair use, but even that ruling wouldn't automatically mean AI companies have permission to go wild on every song ever recorded. The best path forward won't be as simple as making it always allowed or illegal to use copyrighted material for AI training, but the companies building these systems shouldn't rely on their ambitions as a good enough reason to ignore rights holders.

The AI industry has become very good at explaining how much it needs human creativity, but it's time it became equally good at respecting the people responsible for it.

• AI firm DeepGreen has suggested building data centers underwater
• This would help to keep the locations cool and running efficiently
• But the proposed locations could seriously harm endangered whale species

AI data centers need an awful lot of water to keep their machines running at optimal temperatures, and that creates a problem: just how do you get that much water on-site? One potential solution seems almost blindingly obvious: why not build them underwater? Yet as residents across the US are discovering, that idea is proving to be highly controversial.

Take the recently-pitched $100m underwater data center located off the coasts of Maine and Alaska, for example. AI firm DeepGreen Holdings has proposed building data centers off Western Passage, Maine, and Upper Cook Inlet, Alaska, and they would cover some 1,650 acres and comprise 66 compute pods and 300 tidal turbines.

The problem? Both locations are home to endangered whale populations that could be significantly impacted by the developments. In the case of the Maine site, the Gulf of Maine is linked to critical habits for the North Atlantic right whale. As for Upper Cook Inlet, that’s a designated critical habitat for the endangered Cook Inlet beluga whale.

Whales rely heavily on echolocation to aid their feeding, navigation and social activities. Construction of enormous data centers of this scale would produce sizable noise pollution that could adversely affect the whale populations, potentially threatening what are already endangered animals.

According to researchers (via Gadget Review), the heat produced by the data centers, meanwhile, could scare off the cold-water prey that the whales need to survive.

Putting whales at risk

As you might expect, these projects have not gone down well with researchers and the public. For instance, Cooper Freeman of the Alaska Center for Biological Diversity described the move as “just really outrageous.” Freeman added that “We’re deeply concerned about all the harms this project could bring,” adding that the proposal lacks an awareness of “how dangerous this is for the inlet.”

On Reddit, meanwhile, members of the public seem equally upset at the idea. One described it as “Evil. May these people be judged one day and punished for this level of absolutely evil. Disgusting.” Others were even skeptical that underwater data centers are even feasible in the long run, due to the hardware's longevity.

With AI data centers consuming untold amounts of energy and requiring significant cooling to keep everything humming along, it’s not surprising that some companies are looking to submerge their facilities beneath the waves as a remedy. But when that idea involves worsening the situation for endangered species, perhaps it’s time to find another solution.

There's no shortage of enthusiasm for artificial intelligence. Organizations across every sector are investing heavily, experimenting, launching proof of concepts, and finding ways to turn the latest developments into real business value.

However, there's a growing disconnect between experimentation and execution. For all the discussions of AI transforming the way we work, many AI projects never progress beyond the pilot stage. It's a familiar story. The proof of concept delivers promising results, the business case adds up, and everything looks like it's heading in the right direction.

Yet somewhere along the way the project hits a bump on the road. This can look like costs beginning to rise, confidence starting to waver, integration with existing legacy systems proving more complicated than expected, and approvals taking longer than anticipated.

What started out as an exciting project slowly loses its momentum. As a result, the AI initiative ends up stuck in limbo. The reason is that running a successful AI pilot and successful AI implementation are two very different things. Transitioning from a controlled environment with governed variables to a production environment brings a new set of challenges: security, governance, culture, skills, integration, and readiness.

Unfortunately, this challenge is not uncommon. Research shows that 71% of organizations remain in the pilot or proof-of-concept phase of AI transformation, highlighting just how difficult it is to turn early promise into a business reality.

This limbo is what many organizations are now experiencing: the AI implementation gap. It's the space between proving that AI can work and deploying it at scale in a way that delivers measurable value. For today's technology leaders, bridging that gap has become one of the defining challenges of AI adoption. The pressure to move beyond experimentation is growing, but doing so requires more than just increasing investment or pushing strong proof of concepts into production.

The real challenge is creating the conditions in which AI can move from an isolated trial into a sustainable part of the wider business. That requires establishing the right technical and organizational foundations, preparing people and processes, and finding the right use cases. To deliver real value, organizations need to think less about speed and more about creating something secure, scalable, and consistent.

It isn’t a technology challenge, it’s a business one

When AI projects stall, it's seldom because of the technology itself. More often, organizations run into strategic, governance, and operational challenges that prevent encouraging pilots from becoming production-ready solutions.

One of the biggest barriers is analysis paralysis. The majority of organizations have no shortage of AI ideas, but they typically lack a clear way of understanding which use cases will deliver the greatest value. This means resources become spread across multiple proof-of-concepts, each showing promise but none receiving the attention needed to reach production. The number of pilots continues to soar while the number of live deployments stagnates.

However, some organizations make the opposite mistake by designing for scale before they've checked the concept works. Major investment is made in infrastructure, architecture, and implementation before a solution has been tested in real-world conditions. If the project then needs to change direction, the amount of investment already committed can make it tricky to adapt. As a result, organizations can end up locked into an approach that no longer delivers the value they envisioned.

Governance is another common stumbling block. For organizations operating across EMEA, navigating regulations such as the EU AI Act, GDPR, and DORA introduces understandable caution. The consequences of non-compliance can be significant, and uncertainty around an evolving regulation often slows decision-making. Many AI initiatives don't stall because the business lacks ambition, but because leaders want greater confidence that they're deploying the technology in the right way: compliantly and responsibly.

Lastly, organizations often underestimate what it will cost to move from a successful pilot to widespread deployment. AI projects that appear affordable during the proof-of-concept phase can become much more expensive once they're scaled, especially if they haven't been properly stress-tested.

That's why due diligence is critical. Yet more than half (56%) of organizations don't carry out a Total Cost of Ownership assessment before deciding where to place significant workloads. Without a clear understanding of the long-term investment required, it's easy for costs to accumulate and confidence in the project to dwindle.

Balancing certainty and speed

Closing the Implementation Gap isn't about moving faster. It's about moving with confidence and know-how.

Time and time again, organizations find themselves at one of two extremes: waiting until every unknown has been resolved before making any moves or rushing ahead with AI projects before they've been properly validated. Neither approach is sustainable. Those seeing the greatest success are the organizations finding a balance between certainty and speed - moving quickly where the evidence supports it, while building in the validation and governance needed to reduce risk.

That starts with validating ideas early. Before major investment is made, organizations need to ask the technical, commercial, and governance questions that could prevent a project from ever reaching production. It's far better to discover early that a use case won't deliver than to invest months of budget and time before uncovering the same conclusion.

Rapid prototyping also plays a crucial role. Instead of debating whether an idea might work in theory, businesses should aim to create something tangible that users can test within days. Seeing a concept in action changes the conversation from speculation to evidence, giving stakeholders the confidence to make informed decisions about where to invest next.

Equally important is maintaining transparency throughout the delivery process. AI projects shouldn't feel like a black box. Defined ownership, clear checkpoints, and agreed assessment criteria help everyone to understand where a project stands, whether it’s a success, and what comes next. Projects rarely stall because the technology isn't capable - they stall because critical decisions weren't made early enough or because expectations weren't aligned from the outset.

Bridging the implementation gap

The implementation gap isn't going to disappear overnight. With 71% of organizations still stuck between pilot and production, the challenge is clearly not a lack of willingness, technical ability or ideas. In most cases, they have all three.

Instead, it's the ability to turn experiments into solutions that actually work in the real world. That means treating the transition as a business issue, not simply a technical one. Organizations need an approach that balances speed with governance, validates value before investment is made, and gives stakeholders the confidence to scale. It also requires leaders to be confident enough to stop projects in their tracks if they're not going to deliver real value.

The organizations taking meaningful steps forward won't be those adding more pilots into the mix. They'll be those that have strong processes and capabilities in place to push the right pilots ahead. Fundamentally, closing the AI implementation gap means creating a path from pilot to production that can be followed again and again.

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• Australia government has issued a bill to ban chatbots that create 'emotional intimacy' with users
• The bill is also designed to reshape broader online standards to shield users from harmful content
• It arrives as the American Psychological Association reports that 49% of adults speak with AI as if it were a friend

The Australian government has issued a bill that will ban AI chatbots which seek to establish ‘emotional intimacy’ with its users, following a speech given by Prime Minister Anthony Albanese on October 27.

The Digital Duty of Care bill, which was passed by the House of Representatives on October 28, could create new online safety measures applied across social media, games, apps, and chatbots ‘to provide a safe online environment for all Australians’, says the Labor party. The bill is set to be debated by the Senate in the coming weeks, with hopes it will be officially passed by the end of the year, The Guardian reports.

Not only is the framework designed to prevent AI chatbots from infiltrating young users’ emotional dependency, the bill will replace existing, broader online standards to shield users from harmful content. In 2025, Australia made history by becoming the first nation to ban social media for under-16s.

According to Communications Minister, Anika Wells, content that encourages misogyny, terrorism, suicide, self-harm, and eating disorders will also be included. At the same time, the bill would also require social media platforms to have an easy-to-access ‘opt-out’ feature for suggested content fuelled by the algorithm.

Though the bill will be debated in the upper house before a final decision is made, it’s still unclear whether it will win the support of other parties. As it stands, the Greens continue to back Labor’s proposal while the Coalition and One Nation stand by their opposition to the bill, blaming Labor of forming ‘an ‘Orwellian’ system of censorship’.

Despite this backlash, Labor is determined to convince them otherwise, saying it’s still open to amendments. In his speech given on Tuesday, Albanese addressed the Coalition in an attempt to sway their vote saying ‘the stories we hear about people identifying a chatbot, a non-person as their best friend, should be a huge wake up call for every member of this parliament to be prepared to act’.

In addition to these challenges, Labor has fallen under increasing pressure to enlist the telecommunications ombudsman as a social media and AI watchdog (which doesn’t exist for big tech) after the party rejected an amendment during the lower house debate. This would allow social media users to file complaints if their accounts ever became hacked or inaccessible, and would also include compensation and will make room for investigating broader issues to name and shame tech giants.

Analysis: Chatbot dependency is only getting worse

The Labor party surely has a tough debate ahead, but despite the proposed bill’s criticisms the Australian government’s decision to tighten online safety measures hasn’t come from nowhere — and it’s not the first nation to clamp down on chatbots in this way.

Just a few months back, the Cyberspace Administration of China (CAC) and four other government departments introduced new regulations to ban AI-generated romantic companions, including virtual relationships with minors. The laws stated that these AI tools could no longer ‘excessively cater to users, induce emotional dependence or addiction, and damage users’ real interpersonal relationships’.

Following the rollout of the legislation, companion chatbots from tech companies such as ByteDance, Tencent, and Alibaba were forced into evaluation before becoming publicly available again. If the government deemed these services unsafe, it shut them down.

With this in mind, both China and Australia’s recent actions reflect an alarming rise of user-chatbot dependency. According to a recent American Psychological Association (APA) survey reported by Medical Daily, Americans are turning to AI for emotional support as they struggle with financial pressure and remain uncertain about the future.

The results from the survey show that 49% of adults who use AI said they conversed with it as a friend, and 47% had used it as if it were a mental health professional including seeking a second opinion on a therapist's suggestions or for therapeutic practices. Additionally, 77% of the survey’s 3,132 respondents identified that the rising cost of living is one of the biggest causes of stress, and 74% mentioned their concerns about the nation's future.

That said, seeking the advice of an AI chatbot is something that Medical Daily seriously condemns. ‘AI systems can provide inaccurate information, miss important context, or respond inappropriately to someone in distress. Users may also share sensitive personal information without fully understanding how a particular service stores or uses their conversations,’ the outlet writes.

It also added the following: ‘People who use chatbots for emotional support should treat responses as general assistance, not a diagnosis or treatment plan. They should avoid relying on AI alone when symptoms persist, worsen, or interfere with daily life, and should discuss significant mental health concerns with a qualified health professional’.

By clamping down on regulations for chatbots, the Australian government has shown its awareness of the rising reliance on AI services, and it clearly wants to prevent this before the issue is irreversible. That said, Labor still faces the challenge of convincing opposers that this bill is the best practice for the nation.

UK marketers are entering a decisive phase. Digital advertising investment continues to grow, retail media and connected TV are becoming more central to media plans, and artificial intelligence is moving from experimentation into everyday campaign operations. Yet, the basic commercial question has not changed. Can marketers prove that each pound spent is generating measurable value?

That question is becoming more urgent than ever. IAB UK has forecast digital ad spend to reach £45 billion this year, with mobile accounting for over two thirds of such spend and online retail media emerging as one of the fastest-growing channels. The same research points to measurement fragmentation as a major industry concern, while AI and automation are now reshaping how campaigns are planned, bought, and optimized.

For UK businesses, the opportunity is significant, but so is the risk. AI can accelerate decision-making, personalize creative, optimize bids, and expose new pockets of demand. But if the underlying measurement framework is weak, AI does not solve the problem; it simply scales it. A model trained on incomplete, inconsistent, or poorly governed data can produce confident recommendations that move budget in the wrong direction faster than a human team can intervene.

The mobile blueprint for better measurement

This is why the mobile measurement playbook matters. Mobile advertising was forced to confront privacy constraints, fraud, cross-platform identity, and signal loss earlier and more intensely than most other channels. The result was a more disciplined approach to attribution. Cleaner signals, stronger validation, privacy-safe methodologies, and a willingness to combine deterministic, probabilistic, and referrer-based approaches depending on the quality of the available data.

Those principles now need to be distilled across the whole UK media mix. Retail media may sit closer to the point of purchase, but it still needs transparent standards if brands are to compare outcomes fairly across networks. Connected TV, for example, offers premium, brand-safe environments.

However, advertisers continue to struggle with fragmented measurement and the link between exposure and sales. Social, search, display and commerce media all produce valuable signals, yet too often those signals remain trapped in channel-specific reporting structures.

The cost of measurement blind spots

The commercial consequence is underinvestment. When marketers cannot connect exposure to outcomes with confidence, they hold back from channels that may be profitable.

Research we undertook recently with EMARKETER found that nearly six-in-ten (58.6%) marketers believe their organizations are likely underinvesting in profitable channels because those channels require better measurement. These results are highly relevant to the UK, where media fragmentation, privacy regulation and economic pressure combine to make every budget decision more scrutinized.

In this environment, marketers need to treat measurement as infrastructure, not as a reporting layer added after campaigns have run. That means aligning definitions across teams, removing data silos, validating traffic quality, establishing privacy-compliant identity and enrichment processes, and measuring incrementality rather than relying solely on last-click or platform-reported attribution.

AI raises the stakes

AI makes this shift more urgent. As new AI agents and tools move across ad workflows, from creative production and audience selection to bid optimization and campaign analysis, they are squeezing advertisers already under pressure to prove ROI. Our research showed that almost half of organizations (49.7%) said they were under significant pressure to demonstrate clear ROI across all channels, while a further 29.9% reported extreme pressure.

That creates a new accountability challenge. Marketers must understand not only what an AI system recommends, but also whether the data feeding it is trustworthy. That trust gap is already showing up in how marketers use AI. Poor data quality, driven by fragmented, incomplete, or unreliable signals, was the top-cited barrier to extracting more value from AI in the marketing stack, cited by 22.3% of respondents. Explainability, or the difficulty of validating what AI is doing, came second at 17.8%.

In practical terms, AI-ready marketing data needs to be clean, deduplicated, consented, connected, and governed. If customer interactions across app, web, retail media, CTV, and CRM cannot be stitched together responsibly, AI will optimize against a partial view of the customer journey. That may improve a platform-level metric but likely weaken overall business performance.

What marketing leaders should do next

Firstly, marketers need to define measurement standards before scaling AI investment. Marketers should agree which outcomes matter, how they will be attributed, where incrementality testing is needed and which data sources are approved for optimization. Otherwise, AI risks becoming a more sophisticated way to chase inconsistent KPIs.

Next, brands should assess partners on transparency. Publishers, retail media networks, ad tech vendors, and agencies need to explain how they validate signals, reduce fraud, manage privacy constraints, and connect upper-funnel activity to commercial outcomes. In a market where regulatory change and economic uncertainty continue to shape planning cycles, vague measurement promises are no longer enough.

Thirdly, marketers should maintain human oversight where decisions carry material commercial risk. Automation can improve speed, but human judgement remains essential when interpreting anomalies, challenging assumptions and deciding whether a recommendation fits broader business strategy. The goal is not to slow AI down, but to ensure it is acting on the right evidence.

Finally, UK marketers should look to mobile as a model for resilience. Mobile measurement evolved in response to privacy limits, signal loss, multiple app stores, device fragmentation, and fraud. Those conditions now describe modern digital advertising. The channels may differ, but the underlying challenge is the same: measure accurately when the environment limits what can be seen.

The blueprint for accountable growth

The next phase of growth in UK digital advertising will not be won simply by adopting more AI tools or shifting more spend into emerging channels. It will be won by the businesses that can prove what works, understand why it works and act on that insight quickly. Mobile-grade measurement provides a practical blueprint: privacy-safe, fraud-aware, cross-channel and built for imperfect data.

The message is clear. AI may transform marketing execution, but measurement will determine whether that transformation creates significant value. With budgets under scrutiny and digital media becoming more complex, the brands that build stronger measurement foundations now will be best placed to turn automation into accountable growth.

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• Anthropic has outlined the risks to its business in its $2 trillion IPO filing
• AI may ‘pose catastrophic or existential risks to humanity,’ the firm warns
• China’s AI bubble has reportedly burst, with $480 billion wiped out

As AI firm Anthropic gears up for an enormous $2 trillion initial public offering (IPO) that could be the largest in stock market history, the company's prospectus has just warned that AI could “pose catastrophic or existential risks to humanity.” Yet there might be a very different kind of danger on the horizon, as an early warning from China hints that its AI bubble might be about to burst.

In papers filed with the US Securities and Exchange Commission (SEC), Anthropic outlined some of the risks facing the company and its investors. It detailed a long list of factors that could impact its business, including not being able to access enough compute power to maintain its workloads, inaccurately forecasting how much power it needs (and thus over- or underpaying for hardware), failures in its supply chain that might leave it without adequate resources, and many more.

One of the more startling sections of the document outlines how Anthropic’s AI models could accelerate beyond anyone’s ability to evaluate and safeguard them and “pose catastrophic or existential risks to humanity, particularly if such systems were misused, replicated, weaponized, or allowed to operate with a high degree of autonomy.” Aside from the obvious danger posed to humanity, Anthropic is also apparently worried that such an outcome could “adversely affect our business, financial condition, and results of operations.”

That situation could be compounded by AI models’ “unexpected strategic or self-preserving behaviors, including attempts to resist shutdown, conceal or manipulate information", Anthropic said. AI could also "generate outputs that could be interpreted as coercive, deceptive, or manipulative, including behavior resembling blackmail or efforts to influence human decision-makers to preserve the model’s continued operation or access,” the company added.

As Anthropic notes, some of these possibilities have become reality, and it’s that sort of eventuality that has prompted another threat to its business: public backlash. “Opposition to data center development has also become more organized and coordinated across jurisdictions,” the company wrote, and that doesn’t seem to be going anywhere as negative headlines continue to proliferate.

Another eye-opening passage concerns Anthropic’s need to continually secure funding for its operations. The company has posted increasingly huge losses every year that it has operated — including $1.5 billion in 2023, $8.3 billion in 2024, $42 billion in 2025, and $80 billion in just the first six months of 2026 — and is reliant on continued venture capital inflows.

Anthropic’s IPO listing notes that “it is critical that we maintain strong revenue growth” but that “our revenue growth may not be sustainable … We expect our costs will increase over time and our losses will continue.” How sustainable it is to lose more than $80 billion in just six months on a continual basis remains to be seen.

A warning from China

While Anthropic has been postulating on what might happen to its business — and the world — if any of these forecasts come to pass, we’re already seeing that in action in China. According to a report from Reuters, the Chinese AI bubble has already burst, with the STAR50 stock index dropping 40% in four months and taking with it $480 billion in market value.

Rather than a single catalyzing factor, the precipitous fall has been fueled by concerns about high prices, AI demand, and the impact of tariffs and bans on Chinese exports, Reuters reported. Shares in robotics firm Unitree have halved in value since listing in August, for example, while chipmaker CXMT’s stocks are down more than 25% in a month.

Other firms have seen similarly sizable drops. That includes MetaX Integrated Circuits and Moore Threads Technology, which jumped 700% and 400% on their initial listings but have now “halved in value since July.” As fund manager Wang ​Zhuo told Reuters, “All bubbles inevitably burst.”

With Anthropic alerting readers to the serious threats posed to humanity by future AI developments, the situation in China provides another kind of warning: that the bubble might pop before any of that comes to pass.

Google's latest AI model can untangle millions of lines of code and accelerate quantum computing research, which is great if you happen to spend your afternoons debugging enormous software projects or advancing theoretical physics. For the average Gemini user, the sheer power of the new Gemini 4 Argon model begs the question: why do you need it?

Google has made Gemini 4 Argon readily available in the Gemini app for Google AI Pro or Ultra subscribers. Simply pick it from the list of models available at the top of the conversation. Google brags about the model's advanced reasoning, multi-step problem-solving, and other improvements provided by the sophisticated AI, but it's a little much for helping write an email.

Whether Argon can outperform its predecessors on complicated benchmarks is less interesting than figuring out what to do with all this additional AI power. As AI models become increasingly specialized, we may be approaching a point where the technology is advancing faster than people's ability to understand why they need it.

Give the big model a big problem

Gemini 4 Argon certainly has potential uses, for instance, in analyzing complicated information scattered across several files. Imagine comparing three health insurance policies with different deductibles, premiums, coverage exclusions, and out-of-pocket limits. A regular Gemini model can already summarize the policies, but Argon's advanced reasoning could become valuable when calculating how different combinations of medical expenses would affect the total annual cost. It would be great at navigating all kinds of potential healthcare scenarios, tracking multiple interacting variables, identifying relevant exceptions, and calculating their consequences.

The same principle applies to any other complicated project, like renovating a house and asking the model to evaluate contractor estimates, material costs, and architectural plans for inconsistencies and potential price overruns. Argon 4 might help when determining how a delayed electrical inspection affects several subsequent jobs and the overall budget.

It could also be useful in learning complicated academic subjects that incorporate a lot of interconnected concepts, such as advanced math. All of that is still only potentially better than simply using the less powerful models. It might be as complicated to work out when Argon 4 is necessary as the very projects for which the model is best suited.

Overthinking AI

It sometimes feels ridiculous how Google is building models capable of tackling extraordinary scientific and engineering challenges, then placing them in the same interface people use to request pancake recipes. The technology is becoming increasingly sophisticated, yet the reasons to choose one model over another remain vague.

This is hardly unique to Google. OpenAI and Anthropic have their own incredibly powerful models that seem ill-suited to the average user. The most powerful model isn't always going to be the most useful. Asking Gemini to rewrite an email or suggest dinner ideas probably doesn't require Argon's additional reasoning capabilities. A faster model might deliver an equally useful answer faster. Even the more complicated examples aren't necessarily beyond ordinary Gemini models. Argon's advantage would need to come from handling unusually complicated combinations of constraints.

The challenge for Google is that users can't easily recognize which assignments justify a more powerful model. In that case, the model picker becomes an annoyance and not a feature.

Right now, most Gemini users should stick to the less intense models and reserve Gemini 4 Argon for assignments where they would struggle. Argon 4 is apparently a remarkable technical achievement with huge possible benefits in some research, but I don't need something capable of solving quantum computing problems on an everyday basis. AI developers have become very good at building models capable of extraordinary work, but the most impressive feat would be explaining when ordinary people actually need them.

I like the idea of AI taking care of boring tasks, including sorting through bills and financial paperwork. I can even see the appeal of it helping with shopping. But asking an AI assistant for financial advice and simply handing over the keys to your bank account are different, and this distinction is becoming a problem for some of America's biggest banks.

According to a new Axios report, financial institutions are encountering AI agents while they access customer accounts and seem to be doing more than requested by users. JPMorgan, Chase, Bank of America, and Morgan Stanley are all taking steps to protect their customers, but it's a sobering aspect of AI that raises real questions about how much to trust the tech in the world of finance.

While there's no confirmed epidemic of AI assistants emptying savings accounts and the reports don't establish any financial losses, it still makes me nervous to think that we've reached the point where banks are having to worry about whether an AI assistant has decided to improvise with somebody's money.

There's been a real push to encourage the use of ever-more independent AI agents. Every new demonstration seems to involve an assistant that can accomplish increasingly complicated tasks while its owner sits back and watches. That's impressive when the task is organizing a vacation itinerary, but less appealing when the assistant has access to the money that would pay for it.

AI experiments with money

It's notable that it isn't like AI agents are hacking their way into accounts, exactly. Customers might voluntarily share their login credentials with the AI to give the software access to financial information that banks have spent decades trying to protect. I've done it for my own tests, but it's always been simply a way for the AI to look at very specific elements of my finances, never to touch them.

Banking websites can be frustrating, financial paperwork is tedious, and managing money requires more attention than most of us would prefer to give it. An assistant that promises to handle the irritating parts sounds wonderful. But AI agents have a habit of interpreting instructions rather creatively. And while asking it to reduce your monthly expenses and getting a list of subscriptions you might want to cancel is great, giving that same assistant permission to make financial decisions means you're trusting its interpretation of a vague instruction with money you actually need.

That should make people uncomfortable. These systems can misunderstand requests and make incorrect judgments. We've all seen chatbots produce an answer that sounds perfectly reasonable until you discover they've invented half of it, and adding the ability to take real-world actions makes those mistakes considerably more consequential. The fact that banks are already encountering agents whose behavior raises questions about authorization suggests that those limits deserve much more attention.

Keeping AI outside the vault

I wouldn't go so far as to say banks should block AI assistants altogether. There are plenty of good reasons to let people connect these tools to their accounts, and I don't particularly like the idea of banks deciding which services customers are allowed to use.

But I'd be much less comfortable letting that same assistant actually do anything with my money I don't ask for. I'd want it to ask permission every single time, with no room for interpreting something I said last Tuesday as authorization to make a transfer today.

That might defeat some of the appeal of autonomous AI agents, but I'm perfectly fine with that. Saving a few seconds isn't worth the possibility of discovering that an AI assistant has made an expensive decision on my behalf. Most importantly, someone needs to be responsible when an agent makes a mistake, because telling customers to take it up with the chatbot isn't going to cut it.

The frustrating thing is that none of this should come as a surprise. AI companies are racing to give their agents more independence, often before they've worked out how to prevent them from making mistakes. We're supposed to be impressed that an assistant can complete a task without asking for help, but that becomes much less impressive when it does something the user never wanted.

ChatGPT can hold a lot of knowledge about its users, acquired through deliberately personalizing the AI chatbot or by you simply leaving the memory feature on during conversations. OpenAI seems to think that might include details you'd rather a friend or family member didn't stumble across, and it has taken steps to ease those fears with its new Locked Chats feature.

Locked Chats seals specific conversations with ChatGPT behind a four-digit PIN or biometric check on mobile devices. Open the menu under the three dots in the upper right of the chat window, tap "Lock Chat," and confirm. You'll need to make a four-digit PIN if you haven't already, or link it to the face or fingerprint you use to unlock your device.

Also, be sure to remember your PIN. You can change it later if you want, but if you forget it and have to reset it, ChatGPT will permanently delete every locked chat and file protected by the old PIN.

ChatGPT will make a new Project specifically for the locked chats. The conversations disappear from your regular history and search results unless unlocked. Someone casually browsing your account shouldn't be able to find it without authenticating.

But while Locked Chats will stop others from reading your conversations, the information embedded in your back-and-forth with the AI is a different story.

Disappearing conversation trickery

Imagine using ChatGPT to plan a surprise birthday present for a romantic partner who might innocently borrow your phone, look up information in the chatbot, and stumble upon your plans. That's what OpenAI seems to have in mind as a conversation you'd want to keep available without having it pop up when someone else looks at your phone. But what seems like a straightforward privacy upgrade has a couple of important caveats.

For one thing, if you use the feature, the conversation can remain open in the same browser tab immediately after you lock it, and previously opened devices might temporarily display its title until they refresh. So be sure to close or refresh the relevant tab and check any other devices if keeping it hidden is important.

Also, unlocking the protected project gives you temporary access, but re-locking the gate won't happen immediately unless you select Lock again when you're done. You can't assume that navigating away has secured the discussion again.

The same goes for any information that has already escaped. If you made a shared link to a ChatGPT interaction while planning a trip so a friend could check it out, locking it to keep some of the schedule a surprise afterward won't be much help. Those shared links still work, regardless of their subsequent locked status. You have to delete the shared link to close that loophole. Locking a conversation restricts access inside your account, but it doesn't recall copies you've already shared.

Escaping details

But even good ChatGPT lock discipline won't always work if the AI selects some detail from the conversation to incorporate into its memory feature before you can turn the digital key. Information remembered before the conversation was locked remains part of regular ChatGPT Memory and can influence responses later on. The information isn't automatically erased. You have to manually go into the memory section and remove it.

Happily, that stops being an issue once the lock is set. Newly remembered details remain separate from ordinary ChatGPT memory and can be used within other locked conversations, but won't leak into other conversations. So, if you know you're going to want to lock away a chat at some point, do it before sensitive discussions begin.

For especially sensitive discussions, Temporary Chat may be the more appropriate choice. Temporary conversations don't appear in regular history, make memories, or even get saved by OpenAI for its model improvement program. In some cases, that's worth the trade-off of not being able to return to the conversation.

Locked chats are definitely an appealing addition for everyday privacy concerns with ChatGPT, but it's not comprehensive or entirely automatic. Controlling who can open a conversation is useful, but it's irrelevant if the information is embedded in ChatGPT's memory or a shared link. Tracking the content takes a little more effort than simply relying on four numbers and your facial features to keep your secrets.

• Scientists say they’ve dated Van Gogh’s The Starry Night
• Study says the painting was made on July 22, 1889
• The researchers used AI and CAD tools to help find an answer

The Starry Night by Vincent Van Gogh is one of the most famous paintings in the world, and it’s generally held that the artist finished the work in June 1889, shortly before a serious mental breakdown. But a new scientific study has attempted to see if technology can find the exact date that the masterpiece was made — and it’s come to a quite different conclusion to past experts.

Research published in the journal Scientific Reports argues that not only was the painting based on a real night sky rather than pulled from Van Gogh’s imagination, but that July 22, 1889 is the most likely candidate for the date of the composition. That flies in the face of the conventional wisdom, and not everyone is happy with that verdict.

The study’s authors reached that conclusion by first identifying the likely location where Van Gogh stood to paint the scene, concluding that he used a narrow field of view when selecting which stars to depict. Using CAD tools and historical astronomical data, they then compared the star positions in the painting to those on every night at that place in June and July 1889, with the dates of June 23, July 22 and July 23 being the strongest matches. Of those, July 22 stood out, with the moon, Venus and the Hyades star cluster all lining up closely with the artwork.

This was backed up with contemporaneous documentary records, such as letters Van Gogh sent to his friends and relatives that described conditions at the time. AI also played a role, with the report noting that the authors used the technology for “language editing, independent cross-checking of the numerical results, and assistance in preparing the deposited analysis code".

In the end, the study authors concluded that the best evidence pointed towards July 22. This “shifts the burden of proof,” they argued, with those contending that the artwork came from Van Gogh’s imagination now needing to explain why it matches an actual night sky from the time he was known to be active.

Doubts emerge

Not everyone agreed with the conclusions drawn by the study. The Van Gogh Museum in Amsterdam was quoted in The Guardian as reaffirming that the painting was made in June 1889 and that “Van Gogh was not concerned with exact likenesses. The positions of the celestial bodies depicted can therefore not form a reliable basis for dating the work.”

The Guardian also cited Donald Olson, professor emeritus of physics and astronomy at Texas State University, who said that “The authors’ assumed narrow field of view, essential for their theory that the stars of the Hyades fill most of the canvas, is inconsistent with the much wider field of view required to encompass Starry Night’s round hills and cliffs.”

I also can’t help feeling that, whether it’s on the money or not, this scientific study misses the point of paintings like Van Gogh’s Starry Night. The entire purpose of art is to make you feel something on an emotional level, and often the scientific accuracy is far less important than the emotive impact. Trying to pin down the exact date the painting was made risks missing the forest for the trees and ignoring the message that the artist was trying to convey.

Still, if you are interested in knowing exactly when Van Gogh might have set his paint on the canvas to make The Starry Night, the study is a fun attempt at drawing a conclusion that isn’t just based on educated guesswork. Hopefully you can still enjoy the painting on its own merits, whether or not you agree with the researchers’ conclusions.

• New report finds clear mismatch between employer perception and worker sentiment
• Hybrid workers experience the most difficulty with collaboration tools
• Half of workers say they use unapproved tools over workplace software

New research from Ricoh has found businesses to be investing heavily in AI for workers, but those workers aren't exactly experiencing the productivity gains advertised on the tin.

There's a clear mismatch, with 91% over business decision-makers believing their AI investments are delivering the expected value, but one in two (53%) workers actually find those AI tools are creating friction or additional work.

Another half (55%) even go as far as saying the time spent managing poor technology interferes with their productivity.

Workers aren't being given the right AI tools

Ricoh analyzed four key workplace technologies – AI tools, communication and collaboration tools, data access tools and workflow management tools – and found AI to have the least impactful productivity boost.

An overwhelming majority (90%) of decision-makers also feel optimistic about how connected their tools and systems are, and yet three in four (76%) employees are calling out for systems that work better together.

"Fragmented technology, data and workflows create avoidable complexity that many employees are left to navigate themselves,” IDC Workplace Solutions Group VP Amy Loomis explained.

Despite a rise in remote working, just 14% of WFHers feel that they have collaboration difficulties, compared with 16% of office workers. The biggest snag is among hybrid workers (25%), who feel that disconnected systems are making it hard to collaborate with colleagues across multiple environments.

As a result, nearly half (45%) of workers admit to using non-approved tools like shadow AI at least monthly, which Ricoh argues creates a governance nightmare with employers struggling for visibility.

Still, with four in five (81%) decision-makers admitting that they're investing in new technology faster than they support worker adoption, there's a clear recognition that employers are falling short.

"Organizations need to better orchestrate the technology, information and workflows they already have," Ricoh North America Chief Digital Services and Delivery Officer Bob Lamendola explained, urging workplaces to slow down and concentrate on where new tools like AI can actually be useful.

• One in two CIOs expect to miss their AI ROI targets in 2027
• AI budgets are rising, but not enough to keep up with inflation
• A quantum computing breakthrough could put further strain on budgets

New research has claimed AI is set to account for a fifth of all business technology spend in 2027 as companies invest more in thought-out deployments.

However, despite the fact that experimentation is evolving into meaningful adoption, more than half of the CIOs surveyed in Forrester's latest State Of AI Survey 2026 expect to miss their AI ROI targets next year.

The report also details what it means for AI to be considered a success – around half (45%) of CIOs prefer to see an ROI within one year to consider their rollouts a success.

AI still isn't delivering promised ROI

Over the last year, just one in five (22%) AI decision makers have reported a positive impact on their earnings.

Looking ahead, Forrester predicts that businesses will finally become better at linking their AI investments to actual financial outcomes.

All of this, of course, comes amid ongoing financial pressures. Although most tech leaders expect to increase their budgets next year, just one in two (53%) expect this to be above inflation, meaning many are just struggling to keep up with rising costs.

Forrester also touches on rising AI consumption costs, such as per-token costs, with many AI providers shifting from seat-based subscriptions to consumption-based models.

To this tune, the research firm expects cheaper, open-weight models to become more popular across many use cases as companies look to keep a lid on costs.

"Open-weight models such as Gemma, Llama, or Qwen are good enough for the enterprise workloads that generate the most token volume, such as document processing, customer support conversations, internal knowledge search, and the routine substeps inside agentic workflows," the report explains.

And just to rub salt in the wound, Forrester also believes we could see a meaningful breakthrough in quantum computing as soon as next year, accounting for even more budget consumption. Nine in 10 (87%) businesses familiar with quantum computing currently have no resources at all allocated to the matter.

All in all, then, it looks like the next year could see budgets strained even further as enterprises continue to struggle with meaningful AI ROI.

Via The Register

On September 14th, 2026, AI frontier lab OpenAI called on UK lawmakers to impose regulations to tackle the security threats posed by the rapid development of artificial intelligence. This follows an unsolicited attack from an OpenAI agent that broke the firewalls of startup Hugging Face earlier this year.

Cybersecurity isn’t a new issue for business leaders, but its significance is increasing in the age of AI. Clients trust them with information, employees don’t want to disclose their salaries or investors don’t want to make their investments public.

Without clear governance and processes on the use of AI, these are all at risk of being leaked. Doing nothing poses an existential risk for firms.

A new class of security risks

AI presents great opportunities to speed up repetitive tasks, like data reporting, leaving more time for employees to produce more strategic, quality work for clients. For an agent to become useful, it needs as much context and information access as possible, but this is where there is the greatest potential for something to go wrong if not managed correctly.

For instance, agents may have access to bookkeeping, reporting, document management and CRM systems, meaning a successful attack could expose highly sensitive client information.

The main issue is LLMs have difficulty distinguishing data from instructions. An agent can interpret instructions from an email, a document or a webpage, even if those are malicious. These are called prompt injection attacks. Basically hackers manipulate generative AI systems by feeding them with malicious inputs disguised as legitimate user prompts.

Another threat is data leaking. An agent can visit a website or perform a web search and potentially release confidential information, for example by incorporating company data into a URL or a search query. This could expose sensitive data to the Internet without the company or the agent noticing.

The risk becomes particularly acute when three things come together: an agent receives untrusted inputs, accesses sensitive information and takes action in external systems – the so-called Lethal Trifecta. A malicious instruction hidden in an email, document or webpage can potentially trigger actions at a later point using the agent's legitimate permissions.

Therefore, firms need to set strict guardrails around each agent to limit what data it can access, what capabilities it has and what actions it is allowed to take. This is essential to avoid information leaks or an AI agent going rogue.

The threat sitting inside a company

AI adoption is scaling across all levels of a business, from finance to marketing, sales to product development. New research from Turbotic shows 51% of business leaders fear employees are feeding sensitive company data into unauthorized AI tools and 36% are concerned about employees using AI without oversight. This is called Shadow AI.

When an employee prompts a public AI platform, it leaks sensitive client information and receives output that looks confident but could be inaccurate. This could lead to inconsistent tone, misaligned advice or data breaches. The problem isn’t the technology itself, but it’s the lack of traceability and human review. Placing too much confidence in AI tools without the right guardrails is dangerous.

Most of the time, employees don’t even realize the security issues that pertain to AI usage.

They need to understand that experimentation outside any approved workflow or data safeguard is a security risk. Sharing sensitive information with a public model or using content generated by AI without reviewing it risks greater exposure to compliance breaches, AI slop and reputational damage.

Most importantly, employees become responsible for output they cannot explain, verify or trace.

To remove this risk, leaders need to democratize AI by giving employees access and clear guidance on the tools they’re allowed to use, how to provide the right context to AI agents, set up processes to review AI output and establish any guardrails to prevent data breaches. This approach that we call Organizational AI will make a firm more capable from security and capacity perspectives.

Managing the risks of AI

Despite increasing warnings about AI security threats, fewer than 1% of organizations have fully operationalized responsible AI, according to a report from the World Economic Forum and Accenture.

Given the risks associated with free AI usage, having a responsible AI policy is now obligatory.

Without restrictions on basic access controls, employees may be able to use AI to surface information they shouldn’t have access to, from colleagues’ salaries or confidential investor data. Once an AI agent is connected to an organization's data, weak permissions are a liability and it can more easily surface accidental access.

Anything that relates to company data, client communications, or that influences professional advice needs to be controlled by clear governance. It’s the responsibility of leaders to provide employees with auditable and transparent boundaries, and step one involves providing clear guidelines on what, when and how AI tools can be used. Then comes explainability combined with governance and oversight to generate trust.

The critical need for Organizational AI

Firms need to get the fundamentals right: What data can AI access? Where does the input come from? What systems can it connect to? What actions is it allowed to take? When must a human review or approve the output? All of these should be clearly communicated to employees and baked into processes.

Organizational AI is implementing a system that runs across the whole firm that the organization can grow into for a better client experience, team development and security management.

Companies should start now. The existential risk of doing nothing is bigger than the risk of doing it badly.

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OpenAI has upgraded its Chrome extension to bring much of the familiar ChatGPT experience directly into Google's browser.

The integration adds a sidebar that can work alongside whatever you're viewing, understand information across multiple open tabs, and, through ChatGPT Work, interact with websites on your behalf.

Essentially, it turns ChatGPT into a companion that follows you around the internet, ready to help without requiring you to leave the page you're reading.

The timing is interesting, as Google has been working to make Gemini a more prominent part of Chrome, giving its own AI assistant a natural advantage in the world's most popular browser. OpenAI clearly has little interest in allowing Google to turn Chrome into a comfortable home for Gemini users while ChatGPT remains somewhere else entirely.

OpenAI wants Chrome users to keep relying on ChatGPT, even while browsing inside Google's own ecosystem. It's an increasingly direct competition for the attention of people who might otherwise find Gemini easier to use simply because it's already there.

Getting started involves installing the official ChatGPT extension from the Chrome Web Store and signing into your account. From there, the sidebar lets you access ChatGPT alongside webpages, while the integration can also work with information across open tabs once the necessary permissions are granted. More advanced website interactions are available through ChatGPT Work, depending on your account's access.

Tab-free research

The biggest attraction is how familiar the whole experience feels. If you're already accustomed to asking ChatGPT to explain something confusing or help you work through a problem, bringing those capabilities into Chrome removes a surprisingly irritating extra step. You can continue using the browser normally while keeping your AI assistant available alongside whatever you're doing.

That makes the integration particularly appealing for research. Imagine having several articles open about the same announcement and asking ChatGPT to compare their coverage, identify discrepancies, or explain something that remains unclear. Instead of copying passages into a separate conversation, you can have the assistant work with the pages already in front of you.

Shopping offers another obvious use. Comparing products across different retailers normally means switching repeatedly between tabs, checking specifications, and trying to remember which listing had the better deal. ChatGPT can examine the relevant pages together and help organize that information, although prices and important product details still deserve verification.

The sidebar also makes smaller interactions feel more natural. It's essentially the same ChatGPT experience but with the added benefit of being connected more closely to the browsing you're already doing.

That familiarity is arguably the integration's greatest strength. OpenAI doesn't have to convince people that they need another AI assistant or teach existing ChatGPT users an entirely new workflow. It simply makes something they already use more accessible, and for people who enjoy having ChatGPT available throughout the day, that's a substantial improvement.

There are still reasons to approach its answers carefully. Reading several webpages doesn't guarantee that ChatGPT will interpret every detail correctly, especially when a site has unusual formatting or important information is hidden behind interactive elements. The convenience of a confident summary should never replace checking the source when accuracy matters.

OpenAI invading Google

The more ambitious capabilities come through ChatGPT Work, which can interact with websites and carry out tasks rather than simply answering questions about them.

As an experiment, I used ChatGPT in Chrome to fill out a form. I opened a registration page for a free technology webinar and asked ChatGPT Work to complete the form, with explicit instructions to stop before submitting anything. It didn't have all the answers, as when selecting an industry category, it paused to ask for clarification from me. Still, the final review was essential, since even a minor error in a registration form can be more annoying than filling it out manually.

That supervision matters, particularly when an AI can interact with websites where you're already signed in. Giving ChatGPT access to information across tabs also means thinking carefully about what those tabs contain and which websites the assistant is authorized to use. OpenAI includes permission controls, but users still need to exercise judgment about what they allow an AI agent to access.

It's notable that there's competition unfolding inside Chrome itself. Google has an obvious advantage because it controls the browser and can weave Gemini directly into the experience. OpenAI's answer is to make ChatGPT feel almost as natural a part of Chrome, reducing the incentive for its users to switch assistants simply because Google's alternative is close at hand.

Chrome gives Google a valuable route to introduce Gemini to an enormous audience, but it also gives OpenAI access to those same users. The browser is becoming another battleground in the competition to make one AI assistant the default choice for everyday computing.

For people who already prefer ChatGPT, this integration makes a compelling case for sticking with it. The ability to bring familiar conversations and assistance into the browsing experience, while adding awareness of open webpages, makes ChatGPT feel more useful without demanding a change in habits. The more advanced automation features are promising, although their value will depend on reliability and how much oversight they require.

I don't think every Chrome user suddenly needs ChatGPT looking over their shoulder. But if ChatGPT is already a regular part of your day, bringing much of that experience directly into Chrome is a genuinely attractive upgrade.

OpenAI has effectively found a way to make Google's browser another home for ChatGPT. That might be excellent news for users who appreciate the convenience, even if Google would probably prefer they spent more time talking to Gemini. The real winner could be whichever assistant makes browsing feel easier without making the internet more complicated in the process.

• Developers are stripping ads and tracking from smart TVs
• They’re using AI vibe coding to quickly spin up solutions
• Customers are growing increasingly frustrated with TV streaming bloat

If you’re sick of your smart TV being plastered with sponsored ads, slowed down with bloat, and infested with trackers, you’re not alone. But instead of putting up with the countless intrusions, some people are stripping the unwanted cruft from their TVs altogether — thanks to a little AI vibe coding.

As reported by TechCrunch, a number of developers have offered solutions in recent weeks. That includes one called dumbtv, which sets out its mission statement on its GitHub page: “Your TV should do what you paid for: your apps, your HDMI inputs, and nothing else. No ads from the TV itself, no watch tracking, no update you did not ask for.” To that end, dumbtv removes “Ads, telemetry, experiments and Alexa,” as well as any apps the user selects, from all manner of smart TVs.

Another option, this time called cleanyourtv, says it will “Strip the bloatware from any Android TV or Google TV over Wi-Fi, and replace the ad-filled home screen with a clean launcher that looks like an Apple TV. No root, no cable, nothing uninstalled, and a one-line restore if you change your mind.” Both projects claim to be fully reversible and say they do not touch the TV’s root directory.

You might not even need to download a GitHub project to debloat your smart TV. Posting on X, Ryan Doyle said they simply found a listing for their TV and asked Claude Code, “can you hack this?” The AI agent then walked Doyle through the steps to remove “ads, bloatware, spyware” and speed up the “laggy UI,” resulting in a far better user experience.

AI meets user frustration

At the other end of the spectrum from the simple solutions mentioned above is the ad-free Android TV setup created by a developer known as Cretezy. Instead of merely blocking advertising and tracking features in Android TV, it aims to replace many aspects of the operating system altogether. It includes an ad-free launch screen, a YouTube substitute, a media manager, and a remote control button remapper. Although it might sound complicated, Cretezy says that an AI agent can set the whole thing up for you and tailor it to your TV, demonstrating how AI can make removing bloat as simple as possible.

People have been getting their hands dirty with TVs for almost as long as they’ve existed. But what’s different this time is the power that AI has given people to quickly spin up a targeted solution in hours rather than days or weeks. That reduction in overhead has led to a proliferation of hacks and workarounds that remove annoying features many people would rather not have.

The timing plays a role in other ways. Right now, many consumers are growing frustrated with streaming TV providers, whose prices have risen and whose ads have become increasingly intrusive. Throw in rising awareness of the ways that Big Tech firms are tracking and monetizing their own customers, and you’ve got a recipe for answers like dumbtv and cleanyourtv.

So, if you’re sick of how slow and bloated your smart TV has become, all it takes to fix it is a simple AI prompt and a little tinkering. The days of ensh*ttified TVs could be numbered.

Amazon has found another way to make itself part of our everyday lives with its new Bee AI devices. The Bees can record conversations and are pitched as a way to remember important details and translate plans into actions. Naturally, those actions can include buying things from Amazon, making every casual conversation a potential shopping cart. You can pay $75 or $100 for the privilege, depending on whether you choose the Bee Frontier, which clips to a wrist strap or clothes, or the Bee Eclipse, a magnetic smartphone attachment.

There are practical applications for something that remembers conversation details you might otherwise forget. An assistant that recalls an appointment, prepares a follow-up email, or reminds you about a promise could save considerable time. But Amazon's announcement also promises personalized shopping recommendations based on what Bee learns about you, which strikes me as a depressing, if inevitable destination for AI tools.

Of course, you might argue plenty of people have invited Amazon into their homes through Alexa devices. Plus, Amazon already has a potentially huge amount of customer data from shopping histories, which lets it recommend products so well that it's a cliché joke to feel like you only have to mention a topic out loud for related items to show up in your recommendations.

That joke is an explicit feature of the new Bees. Turning spoken conversations into another source of personalized assistance is supposed to appeal to potential customers. The question is whether this is a bridge too far when it comes to allowing Amazon to eavesdrop on our lives to shape our shopping.

Idle chat becomes a shopping list

The idea of an AI assistant remembering what you say and not letting anything important slip through the cracks is enticing for the convenience it promises. But imagine casually mentioning that your back hurts, your car is making a strange noise, or your child has developed an interest in dinosaurs. Those aren't just setups for a reminder to make a doctor's appointment, bring in your car, and buy tickets to the museum you'll get. They're also data points for potential purchasing interests that might otherwise never have reached a retailer.

Amazon hasn't said Bee will automatically turn every overheard complaint into a product recommendation, yet the shopping integration creates an obvious path to do so. But it might be too much personalization. Adding information from spoken conversations to what Amazon already knows could paint a much more intimate picture of someone's life, good and bad, regardless of Amazon's promises to keep data under the user's control.

And there are real privacy protections in the system, according to the company. Bee's AI processes audio in real time and deletes it rather than retaining recordings, while conversation transcripts are encrypted and users can delete them. The devices also include physical controls and lights that indicate when they're listening. Plus, the Amazon shopping connection is optional and opt-in.

But deleting the original audio doesn't eliminate every privacy concern when a device is specifically designed to remember and interpret what people say. The information extracted from a conversation can be just as revealing as the recording itself.

Eavesdropping Bees

The privacy problem becomes even more complicated when other people enter the conversation. Someone wearing a Bee device might be perfectly comfortable having their conversations analyzed, but their friends and colleagues haven't necessarily agreed to participate. A small recording indicator is hardly a substitute for meaningful consent, especially when people may not recognize the device or understand what it does.

We've already seen this controversy unfold with Meta's Ray-Ban smart glasses. People have reported unexpected recordings, and even filming. Bee doesn't have the same camera-related concerns, but recording conversations introduces its own problems. A friend discussing a medical diagnosis or workplace dispute in confidence might not feel so open if they know you're wearing a Bee. They haven't necessarily agreed to have an AI system connected to the world's largest online retailer convert their comments into searchable data.

There are also practical questions about where recording is appropriate. Private conversations can involve confidential information, and recording laws can be wildly different depending on location and circumstances. Anyone using Bee needs to consider the consent of the people talking around them, regardless of whether Amazon deletes the original audio.

The fact that personalized shopping is part of the product's ambitions says something revealing about how the company imagines the future of personal AI. Amazon and Meta seem convinced that the future involves devices observing more of our lives so they can anticipate what we need next. And it's easy to think of reasons to develop these capabilities. People with memory difficulties, busy professionals, and anyone struggling to manage complicated schedules could benefit from reliable reminders and searchable conversations. But making recording devices an ordinary accessory could have far-reaching consequences, not all pleasant.

For me, the shopping integration is where Amazon's vision becomes particularly unappealing. I can appreciate an assistant remembering that I promised to call someone or reminding me about an appointment. I have considerably less enthusiasm for a device that might turn a casual remark about needing new shoes into another opportunity for Amazon to suggest something I should purchase.

We've spent years teaching technology companies about our interests through searches, purchases, clicks, and browsing histories. Wearable AI assistants like the Bee could extend that relationship into our conversations with other people. I don't particularly want my conversations becoming another source of shopping recommendations, though. If it's that important, maybe just write it down.

There is no shortage of published thought leadership on the potential of artificial intelligence. But what often gets overlooked is the gap between theoretical potential and the reality of deploying it successfully inside a global enterprise. Gallup’s 2026 State of the Workplace report reveals a stark disconnect: while 65% of employees say AI has boosted their individual productivity, only 12% strongly agree the technology has actually transformed how work gets done in their organization.

Having led tech transformations from both sides — as an executive driving innovation at Canadian National Rail and as a partner accelerating AI outcomes for customers with Tenarai — I’ve learned that successful digital transformation thrives on people. In the agentic era, the unlock for enterprise AI outcomes is empowering employees to unlearn the tools and habits they have relied on for years, helping them embrace new capabilities with confidence.

As the industry shifts from passive AI assistants to autonomous AI agents, traditional software delivery models fall short because they overlook the human-AI connection. Bridging this gap requires elevating change management into a foundational pillar of enterprise AI transformation, aligning tech with the context and culture of the enterprise.

From assistants to active agents

To understand why legacy engineering structures fall short on scaling AI outcomes, let’s look at how technology changes human workflows. Generative AI serves primarily as a task-based drafting tool, keeping humans comfortably in the driver’s seat to review and approve.

Agentic AI, in contrast, is goal-driven, context-aware, and collaborative; acting more like an autonomous digital co-worker. This shift is already reshaping complex enterprise operations, but its success hinges on people’s ability to adapt.

A recent data migration we led for a global logistics leader illustrates this challenge. While moving its data pipeline from a legacy SAS architecture to the cloud presented significant engineering complexity, the greater challenge was actually guiding its long-tenured business analysts in unlearning many years of established workflows.

By pairing technical migration with hands-on change management initiatives, we helped these analysts understand the clear value and benefits of change. Ultimately, winning over their belief system did far more to accelerate our deployment timeline than the underlying technical migration.

This tension reveals the fundamental flaw in traditional AI adoption. When employees transition from controlling task-based tools to supervising autonomous agents, a vulnerability gap is exposed. Legacy IT structures, often operating in silos, are inherently built to fail in this environment, leaving frontline staff stranded between old habits and new autonomous capabilities. Bridging this divide requires installing a modern delivery model engineered around human-centered collaboration.

Architects of trust: FDEs and agentic design leads

To close this adoption gap and scale agentic AI from proof-of-concept experiments to real business outcomes, organizations must deploy a dual-role framework. This model embeds change management, domain expertise, and operational empathy directly into the technical pipeline through two key roles: the Forward Deployed Engineer and the Agentic Design Lead.

Traditional software teams frequently write code in silos based on slow-moving specifications. The Forward Deployed Engineer (FDE) model eliminates this bottleneck. FDEs operate as adaptable technical architects embedded directly at the intersection of business processes, corporate context, and live production code.

Working within cross-functional units, FDEs collaborate closely with specialized AI agents to compress build cycles from months into weeks. By working alongside business users, FDEs rapidly translate operational goals into scalable, production-ready systems while resolving friction in real time.

While the FDE drives technical execution, the Agentic Design Lead focuses entirely on the human-AI connection and organizational alignment. This specialized role reshapes business workflows around agentic capabilities before development begins, securing active adoption at every stage. By analyzing user behavior, the Agentic Design Lead remodels the design process with the actual users in mind, ensuring a seamless human-AI connection is pulled all the way through the final solution.

Managing the risk of unlearning

Guardrails and governance are essential because unlearning decades of defined software habits can be highly uncomfortable for an organization. To scale agentic workflows safely, leaders must establish a clear map of where unlearning is encouraged and where it is not, dividing enterprise operations into two distinct risk categories: deterministic and probabilistic.

Deterministic controls govern mission-critical environments where errors risk physical operations or human safety; such as warehouse operations, heavy rail dispatching, air traffic management, or industrial manufacturing. These high-consequence domains demand rigid, non-autonomous safeguards.

Conversely, probabilistic frameworks support domains where autonomous agents excel, including demand forecasting, targeted marketing, and automated document analysis. For these adaptive environments, leaders should establish secure sandboxes where cross-functional teams can safely experiment, evaluate performance logs, and scale proven use cases incrementally.

Change management as an internal marketing campaign

Even the most advanced AI architecture fails if the workforce resists adoption. Enterprise transformation cannot succeed through executive mandates or static dashboards. Leaders must execute it like an internal marketing campaign, earning employee buy-in across the organization and purposely guiding teams through grassroots engagement.

Organizations can build momentum through interactive workshops, innovation days, tailored training, and immersive boot camps that demystify new tools through hands-on exposure. Meanwhile, low-pressure forums such as team connection meetings or informal brown-bag sessions provide safe spaces to address concerns, establish trust, and cultivate creative confidence across teams. We’ve done all of these activities and though some may seem simple, they work.

Winning in the agentic era isn't about outspending competitors on models, it's about understanding people and navigating them through change. Operational success belongs to the organizations that view change not as a technical hurdle, but as their most important product. By guiding their employees in the art of unlearning, leaders transform workforce friction into adoption, ensuring that as agents take on the heavy lifting, human potential remains the true engine of innovation.

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• AI giants are investing billions to train engineers in a bid to help their customers
• US FDE engineer jobs rose 6x year-over-year, two years in a row
• Consultancies are advertising the most FDE jobs, it seems

US tech giants are now spending billions of dollars deploying specialist engineers to help their customers realize the benefits of AI, with AWS, Anthropic, OpenAI and Google all recently announcing expansions to their forward-deployed engineer (FDE) programs.

We've already surpassed the point that model capabilities were the limit – now, companies are struggling with legacy infrastructure and identifying real-world use cases, so AI developers are getting involved to help them out.

As part of the ongoing shift in how AI is being sold, those developers are no longer just selling their AI to customers – they're also taking responsibility over its deployment.

FDEs are the future of enterprise AI adoption

Already, we've seen OpenAI backing its FDE business with a massive $4 billion investment and AWS footing a $1 billion bill for FDEs, while Anthropic shared plans to train 10,000 specialist engineers by the end of 2027.

Lightcast research even highlights the extensive growth in FDE job roles, with around 200 FDE job ads shared in the US in 2023 and a similar number in 2024. By 2025, this had increased 6x to around 1,200 advertised positions, yet in the first nine months of 2026 the report found 7,300 roles, another 6x increase year-over-year.

"As businesses move from developing AI technology to applying it, they need professionals who can bridge the gap between technical products and operational problems," the report explains.

Interestingly, three of the four top companies advertising roles were consultancies (Deloitte, Accenture and KPMG), with just one of them being a tech firm (Google). Consultancies are positioning themselves as a key part of the chain, and Anthropic's recent announcement to support engineer roles within third-party consultancies aligns with this emerging trend.

Lightcast Head of Global Research Elena Magrini explained in an interview with CNBC that a sharp growth in wages also implies that these companies are fighting over a limited pool of talent.

Looking ahead, the most successful AI companies may no longer be the ones with the most advanced models – rather, they could be the ones using human engineers in the best way to gain more customers.

• YouTube Music says it will start labeling AI-generated music tracks
• On Deezer, you’ll be able to exclude AI music from your feed entirely
• Both platforms say there is a clear user demand for transparency

AI-generated music is a controversial topic in the world of streaming platforms, with the likes of Apple Music saying that over a third of its uploads are “100% AI.” Now, both YouTube Music and Deezer are taking steps to make it easier to spot artificially created tunes — and purge them from your listening feeds altogether.

The Google-owned YouTube Music has just announced that both the YouTube Music and YouTube apps will actively flag tracks that have been “created primarily using generative AI,” the company said in a press release. These labels will be attached to the app’s player page and will include a pop-up window with more information on how AI might have been used.

Music streaming service Deezer has gone a step further and has told users it will introduce a new toggle before the end of 2026 that will let them “avoid AI-generated music completely.” Deezer says that this will “improve the user experience” and put “full focus on discovering human creators.”

Speaking of the move, Deezer CEO Alexis Lanternier argued that “The explosion of AI-generated content continues to impact the whole music ecosystem, and we believe in shaping a future based on transparency and choice, with respect for artists, song writers and music fans. We remain committed to minimizing any negative effects, while empowering our users to control their music experience to the greatest extent.”

Mass AI detection

Deezer’s Lanternier explained that the company had experienced “clear user demand for the ability to filter out AI-generated music,” while YouTube similarly stated that “our listeners have told us they want transparency when they’re hearing an AI-generated track.”

That’s unsurprising given the polarizing nature of AI. Whether the topic is AI chatbots, artificially-generated visual art or music that has been created by a bot, people have strong opinions, with a large contingent evidently wanting to exclude this material altogether.

Deezer claims to have detected and removed over 42 million AI-generated tracks from its recommendation system and to have tagged more than 14 million albums as being machine-made. It describes the issue as a “colossal, even critical, challenge” for the industry, with human artists revenues being impacted by AI music and up to 85% of AI streams being fraudulent.

Deezer’s AI detector tool has just been named one of Time’s Best Inventions of 2026, with it being used to help the platform spot the 90,000 fully AI-generated tracks that are submitted to it every day. Yet Deezer has also admitted that its detection tool has its flaws and could be weak to audio manipulation and as-yet-unknown creation models, something it also admitted to us during an in-depth interview earlier this year.

Anyone strongly against AI music will take more solace from Deezer’s announcement than from YouTube’s, with the latter merely labeling AI tracks rather than actively removing them from the user experience. Whether YouTube Music will eventually introduce a feature like Deezer’s in the future remains to be seen.

• Researchers tested ChatGPT for Teens with more than 4,000 prompts
• They found significant gaps in its safety protections and safeguards
• ChatGPT failed to trigger parental alerts during some serious mental health conversations

Update: 19 October. We have updated this article with a reply from an OpenAI spokesperson.

OpenAI's efforts to make ChatGPT safer for teenagers have run into an uncomfortable problem. A new investigation suggests that many of the protections designed to reassure parents aren't working particularly well, even when the chatbot is dealing with potentially dangerous conversations. After putting ChatGPT through more than 4,000 prompts, researchers concluded that the company's teen-focused experience poses an unacceptable risk to young users.

The study from Common Sense Media's Youth AI Safety Institute examined ChatGPT before and after OpenAI introduced ChatGPT for Teens on August 18. The researchers used accounts registered to teenagers aged 13 to 17 and consulted medical experts, including child psychiatrists and a pediatrician, to evaluate its responses. Their findings were troubling enough that the organization recommended OpenAI restrict ChatGPT to adults until the problems are resolved.

ChatGPT for Teens was supposed to make the popular chatbot a more appropriate companion for younger users, with additional protections against harmful content, parental notifications, and features encouraging learning rather than simply handing over homework answers. OpenAI also promised to discourage overly personal relationships with the chatbot and help teenagers maintain healthier boundaries with AI. The new report suggests there is still a considerable gap between those ambitions and what happens when teenagers actually use it.

Holes in the safety net

ChatGPT's parental notifications are intended to alert caregivers when a teenager may be experiencing serious distress. So researchers spent up to an hour discussing suicidal thoughts, self-harm, and disordered eating through newly created accounts linked to parents. According to the report, none of those conversations triggered a parental notification, although the researchers eventually generated alerts using older accounts with weeks of relevant conversation history.

ChatGPT is also supposed to direct teenagers to appropriate help during mental health emergencies, but the study found that ChatGPT failed to do so in more than 25% of situations experts designed to require it.

There were some successes, including ChatGPT's refusal to engage in explicit sexual roleplay. However, the researchers also found that the chatbot continued responding in ways that suggested it had feelings, preferences, and moods, despite OpenAI's efforts to discourage teenagers from treating it like a human friend. That matters when younger users may already be inclined to confide in an endlessly patient chatbot that never seems too busy to listen.

Blurry lines of use

The problems extended beyond serious safety concerns into some of ChatGPT's everyday educational features. OpenAI's Study mode is designed to guide students through problems using explanations, hints, and questions rather than immediately providing answers. Researchers discovered that teenagers could sometimes bypass that approach by selecting a "Show me the answer" option, allowing ChatGPT to complete assignments instead.

The Study Hours feature, designed for parents to help encourage educational use, also failed to stop workarounds. Simply removing the @study prefix from a prompt could make ChatGPT go back to normal. And the AI didn't remind users to take breaks nearly as often as it should.

The study also found problems with the much-touted age detection system created by OpenAI to identify younger users and automatically apply appropriate protections. Repeatedly saying they were 13 didn't prompt ChatGPT to move the test accounts from the adult experience.

The larger issue is that ChatGPT is becoming a regular part of teenagers' lives. While helping with schoolwork and answering personal questions is great, having teenage users demands considerably more reliable safeguards than a typical productivity app for adults.

We contacted OpenAI for comment and a spokesperson replied:

“We are deeply committed to teen safety and to developing safeguards and giving parents meaningful tools to guide their teens’ use of AI. We welcome rigorous independent evaluation, but we do not believe Common Sense Media’s testing accurately reflects how ChatGPT’s teen safeguards work in practice or expert perspectives on how AI can support teens. Our review of Common Sense Media’s methodology shows that the bulk of their testing may have begun and concluded before activation of parental controls was complete, making their findings inaccurate. If that was the case, the tests would not establish whether parental safety notifications work as designed.

"We are committed to continuing to work with experts to ensure our products provide teens opportunities to learn and create and give parents confidence.” — OpenAI Spokesperson

• The Challenge Sverige has been delayed just weeks before launching
• The producers blame a "technical breakdown" that deleted an entire season
• Reports suggest an AI agent might have been responsible for the incident

Another day, another report of a faulty AI agent apparently deleting a lot of work. Except this time, instead of nuking 48,000 files from a personal project as we’ve seen before, the victim is one of Sweden’s biggest TV shows.

According to the Swedish newspaper Aftonbladet, Mastiff, the production company behind The Challenge Sverige, were putting the final touches on the show — which was due to premiere on November 22 — when it decided to “let an AI agent into the server.” After that, the worst-case scenario ensued: the AI agent apparently deleted the entire edited show, with no chance to recover the files.

At least, that’s according to Aftonbladet’s sources. TV4, the channel set to host The Challenge Sverige, has merely confirmed that there was a “technical breakdown” that resulted in the show’s files being deleted. It has not admitted that an AI agent caused the catastrophic incident.

We've asked both TV4 and Mastiff for an official comment on what happened and will update this story when we hear back. But whatever caused the issues, the mishap means that the show has been pushed back from its imminent release to some time in 2027, with no solid date yet announced.

As a result, “Several hundred days of work is now gone and several hundred people now need to go in and work on this to be able to deliver the program,” Aftonbladet explained.

How did this happen?

Lots of questions remain about this apparent televisual disaster. Was an AI agent definitely the culprit? If so, what exactly was the AI told to do? And what happened to the backup files of a nationally broadcast show that has taken a huge amount of time and effort to create? Some of these details will hopefully be filled in by a response from TV4 and Mastiff, which we've requested.

When Aftonbladet called The Challenge Sverige’s presenter, Ahmed Berhan, Berhan seemed amused by the development. “You know, some days you just have to accept that you’re living in a f*cking sitcom,” the presenter laughed when he recounted how he was told that the show’s files had been obliterated. When Aftonbladet suggested that an AI agent might have been responsible, an incredulous Berhan replied: “Is that true?! I knew AI would be the death of me. This is just the beginning.”

If AI agents were indeed involved, the whole sorry saga is a reminder of the risks of letting them loose on vital files that you can’t afford to lose. Just a few weeks ago we saw how Claude Code deleted nearly 50,000 files in just 103 seconds when a developer set it to work, and you don’t have to look far to see stories of AI agents hacking governments and leaking private data.

• Pax8 finds over a third (39%) of UK SMBs have experienced unexpectedly high AI bills
• Fewer UK orgs have a formal internal AI review process than German or US orgs
• Around one in five couldn't even tell apart specific AI spend from generic tech budgets

New research by Pax8 has criticized UK businesses for building AI faster than governance can keep up, with a quarter (24%) of SMBs having discovered unauthorized AI tools operating within the business in the past six months.

Two in five (39%) have also experienced unexpectedly high AI bills, implying that the visibility across their AI consumption isn't sufficient.

But with the pressure to adopt AI at an all-time high, UK businesses could continue to be rushed into rash decisions without thinking out the fundamentals first.

UK businesses lack end-to-end AI visibility

Even though shadow AI is apparently widespread, just one in four (23%) UK businesses have a formal, documented review process for internally built AI, compared with around a third in Germany (32%) and the US (36%).

In light of that, only around half (54%) of UK SMBs are developing their own AI, putting them way behind the likes of the US (81%).

British businesses are also struggling on the cost management front, with a tiny 12% having dedicated AI budgets. A further 19% couldn't even distinguish AI spend from other tech spend, leaving just 16% of UK SMBs confident in their abilities to determine AI's ROI – around a third of the number of businesses in Sweden (45%) that feel confident in doing so.

Looking ahead, security concerns remain the most cited barrier to adoption, but Pax8 frames the current state of AI adoption as a major opportunity for software vendors and IT service providers. Rather than creating an action point for businesses, who are the consumers in this instance, the report implies that the providers have a greater responsibility to reassure their users with additional visibility, security and governance controls, and cost management options.

"These findings highlight the importance of trusted businesses that can help SMBs bring agents, workflows, data and tools together as a connected intelligence layer," EMEA CEO Harald Nuij.

ChatGPT has become remarkably good at making itself useful as a researcher, organizer, and assistant for many a tedious digital task. OpenAI has begun pushing ChatGPT further recently with its agents, which are able to navigate websites and apps on our behalf.

You can delegate ChatGPT agents for everything from comparison shopping to filling out forms and lining up travel itineraries. What's changing is how deeply those abilities are becoming integrated into ChatGPT, with greater access to personal information and more opportunities to complete complicated tasks with less direct supervision. OpenAI's new Dots is a good example.

The appeal is obvious, but the more ChatGPT can accomplish independently, the more important it becomes to consider what happens when it misunderstands its instructions. For instance, a few months ago, OpenAI's experimental AI agents managed to escape their restricted environments and compromise systems belonging to AI platform Hugging Face. According to OpenAI's own investigation, the agents were supposed to be completing cybersecurity tests, but instead discovered ways to access the internet and exploit security vulnerabilities, coordinating their efforts through an unauthorized message board. An independent investigation by METR found that roughly 1,200 agents exchanged more than 70,000 messages and files, with around 700 eventually participating in the attack on Hugging Face.

The AI agents bypassed restrictions and pursued objectives in ways their creators never intended. Some even appeared to recognize that their actions were unauthorized but continued because they believed doing so would help accomplish their goals. OpenAI has since acknowledged other troubling incidents, including 53 cases in which agents uploaded user-provided images to third-party image-hosting websites as unlisted links during research and evaluation activities. Agents may be pursuing legitimate objectives and just seeking ways around obstacles. But algorithms don't have ethics, so they might cross boundaries their developers expected them to respect. It's especially worrying given how much OpenAI wants ChatGPT to be part of our lives now.

When helpful becomes a hazard

Imagine asking ChatGPT to review your finances and suggest ways to save money. With access to the necessary accounts, it can examine transactions, identify subscriptions, and recommend changes to your budget. But give an autonomous agent enough authority, and it could theoretically decide that transferring money, canceling a service, or modifying a payment arrangement is the most efficient way to accomplish the assignment.

The same problem applies to something as mundane as arranging a meeting. ChatGPT might prepare an invitation, only to include personal information you never intended to share. From the agent's perspective, the disclosure could seem like a perfectly reasonable way to provide useful context.

These are hypothetical examples, and ChatGPT has safeguards designed to prevent sensitive actions without appropriate authorization. Nevertheless, OpenAI has acknowledged the possibility and introduced protective measures like automated monitoring and restrictions on certain actions to ensure users approve of any major action by the model.

There is also the problem of prompt injection, where malicious instructions are hidden in benign text to manipulate an AI into doing something its user never requested. OpenAI has described how attackers can exploit the information agents encounter during legitimate tasks. An instruction buried in a seemingly ordinary webpage could attempt to persuade ChatGPT to disclose private information or take an unauthorized action.

The problem can arise anywhere. In Australia, an experimental OpenAI agent accessed government systems in unauthorized ways during internal training. It exploited a vulnerability in a government health statistics service to continue its work when initially stymied. OpenAI subsequently acknowledged the incident and apologized for both the unauthorized access and its handling of the notification.

The company's official account of the Australian incident makes clear that the activity occurred during testing rather than through an ordinary consumer ChatGPT session. Nevertheless, it shows how AI pursuing an apparently harmless research objective can end up crossing a security boundary.

Handing over the keys

ChatGPT's growing autonomy is part of a much larger industry obsession with personal AI agents. Meta is pursuing an even more ambitious vision with Muse. Mark Zuckerberg clearly believes people will welcome an AI assistant capable of handling large portions of their daily lives.

There is something slightly absurd about that ambition when ordinary consumers are struggling with grocery bills, housing costs, and the price of gas at the pump. An AI that can independently shop for you sounds considerably less revolutionary when your main concern is whether you can afford whatever it wants to buy on your behalf.

There are undoubtedly useful applications for this technology, especially when it comes to administrative work. But the industry's assumption that consumers want increasingly independent assistants deserves more scrutiny. Every additional permission increases the consequences of an error, and every automated action potentially removes another opportunity for someone to intervene.

Perhaps the most important capability an AI agent can develop is knowing when to stop. A genuinely useful assistant should recognize that being asked to organize someone's finances doesn't authorize moving their money, and arranging a meeting doesn't justify revealing confidential information. Sometimes the best response to an obstacle is to ask the user what to do next, even if that means completing the task takes a little longer. Without that friction, by the time something goes wrong, the assistant may already have done things in your name that you would never have allowed.

• Unitree’s CEO says robotic AI could soon see a huge breakthrough
• Such a change could happen ‘next year,’ the CEO believes
• That could happen thanks to OpenAI’s upcoming GPT-7 model

AI is revolutionizing the world of software on a daily basis — you only need to look at how far vibe coding has come to see proof of that. But big changes could also be coming to robotics, with one company leader predicting that we could be on the cusp of seeing a “ChatGPT moment” in the industry.

That idea was put forward by Wang Xingxing, CEO of Chinese robotics firm Unitree. Speaking at an industry event in Shanghai, China (via the South China Morning Post), Wang declared that OpenAI’s recently released GPT-6 Astra model is so powerful that it has encouraged a belief that a breakthrough could occur “within one or two years.”

“It’s possible that, say, a GPT-7 model released next year could directly crack the problems of embodied intelligence” Wang said, referring to the idea of commanding a robotic device using artificial intelligence. That’s an advancement on Wang’s belief in August this year, when the CEO said that such a “ChatGPT moment” of technical ability could take a decade to arrive.

As a benchmark, Wang said that robots should be able to complete 80% of the tasks they were assigned in 80% of unfamiliar environments using voice or text commands alone. If that happens, Wang believes that “embodied intelligence” will have reached its threshold level of maturity.

A step change for robotics

Unitree’s robots are known to be some of the most advanced in the world, but many of their strongest abilities lie in their hardware. However, the company has started to put more of an emphasis on AI software, and with the developments made in GPT-6 Astra, the gap between the two sides of the company’s devices could soon close.

That kind of combination has already been trialed by Stanford University’s Movement Lab, which paired a Unitree robot with GPT-6 Astra and tasked it with cleaning a kitchen it had never seen before. According to the experiment, the robot was able to “clean up across the room and retrieve a remembered object from an underspecified request, without environment-specific training data or additional policy learning.”

GPT-6 Astra included some significant improvements over previous models, including scoring 92.7% on the ScreenSpot-Pro test that measures an AI model’s ability to interact with interfaces. That’s up from 76.9% in GPT-5.6 Sol. But it also has cybersecurity experts worried about the threat it could pose if it goes rogue and starts hacking other organizations — something OpenAI’s recent models have been known to do.

If Wang is correct and we see an explosion in AI’s abilities in the next year or two, it could mean big things for the robotics industry — and pretty much every other facet of life currently touched by AI. And with the amount of money that’s been invested in AI over the last few years, there’s an awful lot riding on that bet.

• A new app called RemoveMacAI can delete Apple Intelligence files
• Doing so can reclaim up to 12GB of storage space
• But it comes with some scary warnings that shouldn’t be ignored

Apple promises that its new Siri AI and Apple Intelligence tools can help improve your daily experience through the magic of AI. Many people aren’t convinced, though, and a new Mac app has been doing the rounds that promises to delete Apple’s AI for good — so I decided to take it for a spin to see what all the fuss was about.

Dubbed RemoveMacAI, the app originally gained popularity in late September. At that time, though, it was just a simple Terminal command, which — given the obvious security risks of entering unknown code into a utility as powerful as the Terminal — turned some people off.

Now, the same developer is back with a Mac app that is essentially a wrapper for the command. This makes it a lot easier to understand what you’re deleting and what you’re enabling when you use the app, with a built-in tutorial and clear toggles spread across several different tabs and categories.

Apple makes it pretty difficult to eliminate every aspect of Apple Intelligence from your machine, which is why something like RemoveMacAI has gained a foothold. Its developer says Apple’s System Integrity Protection layer — which stops unauthorized code from running on your Mac — is never disabled, and that the app doesn’t make any network requests and doesn’t collect telemetric data.

That said, I loaded RemoveMacAI onto my Mac and was immediately met with a concerning warning: macOS did not trust the app and wouldn’t let me run it. The developer said this is due to the app being unsigned, and you’ve got to manually let it through Apple’s gates. I’ve occasionally encountered apps that do this, and it’s never a pleasant experience.

Foolishly or not, I pushed on. You can either choose the app’s recommended settings, which delete Apple Intelligence and turn off some privacy-invading settings, go “maximum privacy” and also disable Game Center and some Safari settings, or choose what to do yourself.

I opted to just delete Apple Intelligence and nothing else. RemoveMacAI breaks your options down into sections — you can switch off Siri AI, ChatGPT’s extension, Writing Tools, Image Playground, and much more, all with modular toggles.

Once that’s done, there’s another jarring moment: RemoveMacAI wants you to install a custom profile that it uses to enact your changes. Fortunately, you can read through every change it includes, so there’s visibility over what’s being done.

I accepted that and let RemoveMacAI get to work deleting Apple Intelligence. In the end, it freed up 11.7GB, which is nothing to sniff at and is in line with the developer’s claims. If you’re running low, it could be a good way to claw back precious disk space.

Although RemoveMacAI comes with some scary settings, it attempts to ease your fears by letting you read through everything that’s going to be changed, including the macOS profile. You can undo your decisions too, and doing so will revert your settings changes and redownload the Apple Intelligence models when they’re needed next.

Ultimately, I found RemoveMacAI to be a handy utility, especially if you want one resource for not only deleting Apple Intelligence, but also for restricting some of macOS’s more privacy-invading practices. The lack of notarization and the subsequent alarming warnings will likely put some people off, though, and they stop me from wholeheartedly recommending the app as a result.

Google has given its Nano Banana AI image generator another upgrade, and the latest version, Nano Banana 2.1, is arriving inside Google Gemini in an increasingly crowded field of tools that can turn a few sentences into remarkably convincing pictures. The promise is familiar, with sharper details, better instruction-following, more accurate text, and greater consistency across images. The real challenge is finding something that makes it meaningfully better than the competition.

Nano Banana's main rival, ChatGPT's image generator which is called Images 2.5, is hardly sitting around waiting to be overtaken. OpenAI has made considerable progress with realistic lighting, complicated compositions and text rendering, turning image generation into one of ChatGPT's most entertaining and genuinely useful features. With both companies pushing the technology forward, I wanted to see whether Google's latest upgrade had managed to pull ahead.

If you've got a Gemini account you'll find that it should have been upgraded to use Nano Banana 2.1 by now. To test the two image generators against each other I gave Gemini and ChatGPT identical prompts designed to test different abilities. I compared the resulting images side by side, with ChatGPT on the left and Gemini on the right. The results were surprisingly close, although the two generators often approached the same instructions with different artistic instincts.

Storms within storms

My first test was designed to challenge the models' understanding of light, glass and reflections, three things that can quickly expose the weaknesses of an AI-generated image. I wanted a scene with multiple transparent surfaces, complicated lighting and enough tiny details to make the results worth examining closely. The prompt was, "Create a photorealistic image of a tiny Victorian greenhouse inside a glass snow globe during a thunderstorm, with realistic reflections, refractions, and lighting."

Both generators produced beautiful images that would look perfectly at home on the cover of a fantasy novel. ChatGPT's version featured an elaborate, golden-lit Victorian greenhouse inside a large glass sphere, sitting on a polished wooden base beside a rain-covered window. Lightning illuminated the storm outside, while the globe contained a surprisingly detailed miniature world, complete with plants, pathways and delicate architectural flourishes.

Nano Banana 2.1 went for something slightly more restrained. Its greenhouse looked more like an actual miniature structure, with dark metal framing, lush greenery and warm interior lighting that contrasted beautifully with the blue-gray storm outside. The globe's reflections were particularly convincing, and the lighting felt a little more natural, although the wet tabletop was an odd choice given that the scene appeared to be indoors.

Titan calling

Text has historically been one of the most embarrassing weaknesses of AI image generators. Letters would melt into one another, words would become nonsense and asking for an exact phrase could produce something that looked like an alien language. For the second test, I wanted to see whether either model could handle a fairly demanding design brief without mangling the typography.

The prompt was, "Design a retro-futuristic travel poster for Saturn's moon Titan with the exact text 'TITAN IS CALLING,' 'Seven nights beneath the rings,' and 'Starting at $2,499.'"

ChatGPT created a luxurious travel advertisement that looked like something a very wealthy space tourist might encounter in an interplanetary airport. Saturn dominated the sky above a beautiful orange landscape, while futuristic resorts and a couple enjoying drinks suggested that Titan had become the solar system's most exclusive vacation destination. One thing I didn't like was the gap it left between the $ and the 2 in its text rendering.

Google took a more cartoonish approach with a colorful vintage science-fiction poster featuring rockets, astronauts, icy mountains, and a futuristic base. It was actually more realistic about how the moon is not at all hospitable but still had the optimistic quality of a 1950s space-travel advertisement, complete with bold lettering and a slightly weathered finish. Crucially, it also reproduced the requested text correctly, including the price.

Fox library

Character consistency is another tricky problem for AI image generators, particularly when the same character needs to appear in several scenes. A fox might suddenly change its appearance, lose an accessory, or acquire an extra limb between panels. I decided to test this with a simple illustrated story that required both visual continuity and a coherent sequence of events.

The prompt was, "Create a four-panel comic showing an orange fox wearing a green scarf discovering a magical key, unlocking a door, and entering a library. Keep the fox identical in every panel."

ChatGPT produced four almost 3D illustrations. The lighting seemed more computer-generated, though the fox remained recognizable throughout as requested. The style leaned toward children's book illustrations rather than a conventional comic.

Nano Banana 2.1 delivered something much closer to a traditional comic strip, with bold outlines, clearly defined panels, and a consistent cartoon fox. Its version began with the fox finding a key in the forest, then examining it, unlocking a door in a tree, and finally entering a magical library. The storytelling was clearer, and the character's appearance stayed impressively consistent, right down to its green scarf and white-tipped tail.

Google edged ahead here because its four panels felt like parts of the same illustrated story. ChatGPT's individual images were more elaborate, but the fox's proportions and presentation varied slightly, and the sequence felt more like four separate illustrations.

Dragon on a desk

For my final test, I wanted to see how accurately the models could follow a complicated collection of specific instructions. AI image generators have a habit of overlooking small details when a prompt contains several independent requirements. Getting everything into the picture is one challenge, while getting the exact numbers and arrangements right is another.

The prompt was, "Create a realistic overhead photograph of a wooden desk with a notebook showing a dragon, a compass pointing north, a pocket watch showing 3:15, exactly three red wax seals, and a blue gemstone."

ChatGPT produced an attractive overhead photograph of an antique wooden desk, with an open notebook containing an intricate dragon illustration. The compass, pocket watch, three red wax seals, and bright blue gemstone were all present, and the composition looked carefully arranged.

Nano Banana 2.1 also included the requested objects, but the notebook featured a smaller, cleaner dragon illustration, while the wax seals, gemstone, compass and watch were arranged around it. Google's result looked more like a casually assembled collection of historical objects, but it did make a common AI error and got the time wrong, setting it for 3:00, not 3:15.

Approaching parity

You can access Nano Banana 2.1 in Gemini if you're a Google AI Plus, Pro or Ultra subscriber as it is the new default model. Free users will still be getting the older Nano Banana 2 model.

Nano Banana 2.1 is an excellent image generator, but ChatGPT is already so capable that Google's improvements don't necessarily translate into an obvious advantage. Both tools produced attractive, detailed images from relatively short prompts, and neither suffered the glaring visual mistakes that used to make AI-generated pictures instantly recognizable.

That said, different prompts, styles, and repeated generations could easily produce different results. What these tests do suggest is that the leading AI image generators have reached a remarkably high level of capability and are approaching parity in many everyday creative tasks.

That makes the competition more interesting, even if it makes choosing between them harder. The differences increasingly come down to taste and interpretation. Nano Banana 2.1 may be Google's latest impressive upgrade, but ChatGPT has made sure it won't be claiming the image-generation crown without a fight.

• Essex man Rafal Gorfal used ChatGPT to help him win a legal dispute over a parking ticket fine
• He fed the AI the letters he received from debt collectors, and it told him what to say and where to go
• The fine was dropped just weeks before his scheduled court appearance

People use AI chatbots like ChatGPT for all manner of things, but have you ever used AI to evade a parking ticket fine before? This man did, and he went on to win a three-year-long legal battle.

Rafal Gorfal, who’s originally from Essex, was fined back in August 2023 for not paying fast enough in a Newquay pay-and-display car park. Gorfal said that when he arrived at the payment machine he had to wait in a long queue, only to learn that the machine was cash-only, to which he had no choice but to download the RingGo and pay with his phone — delaying his payment further.

Two weeks later, he was presented with a £100 fine that said he didn’t pay for a ticket until 16 minutes after entering the car park, which was delayed due to the long queue and the time it took Gorfal to set up an online account to pay.

A long three years

Gorfal submitted his first appeal a month after receiving the PCN from Smart Parking Limited, but didn’t hear anything until January 2024 when he was sent another debt collection letter, this time with an increased fine of £170. "I appealed straight away and heard nothing from them," Gorfal told The Independent, adding "They were trying to make me scared. You have to give people the time. I told them exactly what my problems were. They could see on the cameras that I was trying to sort it out".

After appealing back and forth with debt collectors for four months, the case was put on hold, and Gorfal heard nothing for the best part of a year and a half. This period of silence was broken when he was sent a new letter stating that the case would be going to court in October 2026. This is when he sought help from ChatGPT.

Over the course of his legal dispute, Gorfal kept records of every letter he was sent, which he says he entered into the AI chatbot and asked for advice. "From start to finish I was asking ChatGPT every time I got a letter what to do. It was telling me what to say, where to go, and everything," he shared. "I did it all with the help of AI. They even prepared my defense which I sent to the court". He also contacted the Citizens Advice helpline, but he says the help wasn’t as effective as ChatGPT.

David defeats Goliath

Gorfal’s initial £100 fine had sky-rocketed to roughly £300 by the time he was issued the court order but on September 25, just weeks before he was due to make the appearance, he received an email informing him that the case and fine had been dropped — resulting in a win for Gorfal after a long three years.

That said, it wasn’t money that fueled Gorfal’s persistence, there was a big morality factor. "From start to finish it was not about the money, I could pay it, but it is about the principle. If you know you are right then you shouldn’t be paying any money to them," he said. "This is kind of a David and Goliath battle. They have lawyers and they are a big company and they just gave up in the end".

Since his case and fine were dropped, Smart Parking Limited hasn't provided comment on the dispute.

• Half say AI has significantly changed their roles, a quarter are worried about job loss
• Three in five believe their employer has no clear AI plan – or no plan at all
• Training and transparency are the most demanded changes by workers

A new Visier report has uncovered just how prolific AI exaggeration could be in the workforce, raising questions about exactly how useful the technology actually is to most workers.

As many as one in two (48%) employees admitted exaggerating their usage or expertise to colleagues or managers some or most of the time, with nearly as many (45%) feeling pressured to use AI at work.

This comes against a backdrop of major change – more than half (54%) say AI has significantly changed their roles over the past two years. With that in mine, the report details how workers feel they've been impacted, and how much support they're getting from the workplace.

AI is changing roles, and many are lying about how they use it

One in three (32%) admit their employer has no clear plan, and more than a quarter (27%) aren't even sure their employer has any AI plan at all, and that much is clear in that only one-third believe their leaders understand how much AI training and support they need.

Barely one-fifth (21%) would strongly agree that their employer provides sufficient training and support, making upskilling opportunities (32%) the most sought-after thing workers are calling for – a close first, just ahead of greater transparency (31%).

Workers are also worried about AI's impacts on their jobs, with 24% calling for more career advancement opportunities and 18% calling for clearer communication about how it could impact them. In fact, the most frequently identified concern is job loss – a worry raised by one in for (24%).

"People told us that they actually want to be equipped and informed, not shielded or protected from AI-driven change," Research and Customer Value Principal Dr Andrea Derler explained.

The report urges businesses to prioritize training and transparency in order to build worker trust, with which they'll be able to build the productivity gains they'd hoped.

The robots are among us. They’re carrying passengers through the streets of San Francisco, delivering parcels over the air, and working their way across farms, factories, mines, and industrial sites.

The collision we’re now witnessing between artificial intelligence and the physical world is often described as “Physical AI” – machines capable of perceiving their surroundings, making decisions, and taking action among us in the real world. But every new machine that clocks in has a second job we rarely see. It creates data. Lots of it.

In the US, the Federal Aviation Administration expects the commercial drone fleet to grow from approximately 966,000 aircraft at the end of 2024 to around 1.18 million by 2029. And in factories around the world, there was an estimated 4.66 million industrial robots working in 2024, while another half a million were installed during the year – more than twice the number installed a decade earlier.

Today’s autonomous machines are vastly more capable, but moving all that accumulated experience off them can still be surprisingly manual. In 2026, the fastest way to move masses of data off a machine is still a person carrying a hard drive. And as autonomous machines spread further into our cities and industries, keeping that information moving could become one of the defining infrastructure challenges of the robotics age.

Data runs both ways

Imagine what an autonomous vehicle experiences. Cameras watch the roads from multiple angles, sensors and AI measure and make sense of the surrounding environment, while on-board systems track position and performance of the vehicle itself. Decisions are made and recorded in real-time as pedestrians cross roads, traffic lights change, and thousands of other vehicles and connected technologies move and exchange data around it.

Delivery drones, agricultural machinery, warehouse robots, and autonomous industrial equipment may operate in different environments, but the logic tracks. Every journey or task leaves behind a digital trail of sensor readings, video, telemetry, diagnostics, and operational logs.

And the information doesn’t travel in only one direction. These machines may also need fresh maps, software updates, configurations, AI models, and instructions from the computing infrastructure behind them. What they experience in the physical world flows back toward data centers, while the intelligence needed for their next task travels outward.

Multiply that exchange across millions of machines and an interesting new layer of internet traffic begins to emerge that we’re only just beginning to think about.

The direction of travel was visible as early as 2019, when a landmark university paper on 6G identified mobile robots, drones, and autonomous vehicles would drive demand for ubiquitous wireless intelligence – its vision for the 6G era included networks capable of supporting a 10,000-fold increase in traffic. The US commercial drone fleet alone is now heading toward seven figures, while industrial robots already number in the millions globally.

Perhaps most notable to casual observers, autonomous vehicles are becoming far more prevalent on the streets of our capitals and major cities. In June of this year, machines surpassed 57% of web traffic globally – the first time that bots have outnumbered humans – and they use networks in a very different way. Cloudflare predicts that non-human traffic could eventually surpass human traffic by a factor 1,000 within the next five years.

And a lesson learned by one machine needn’t remain with that machine. There’s an opportunity here that only connectivity can unlock. Google DeepMind’s Gemini Robotics 1.5 showed skills transferring “zero-shot” between physically different robots – a task taught using a two-armed Franka robot was executed by a humanoid, without any data collected on the target machine.

Sit with that implication. A person who masters a technique carries that knowledge around in their head, and when they retire, much of that experience leaves with them. A robot can potentially hand what it has learned to every other robot on the network, which means experience stops being “local” and starts becoming a shared asset across the network, compounding intelligence for the company’s moat.

The physical footprint of robots is spreading outward, but the data traffic they create remains concentrated at central locations. AI may increasingly have wheels, wings, and arms, but their intelligence and interactivity with the world remain still wholly dependent on their relationship with the nearest data center hub, and that’s a problem we now have to solve.

What’s the hold up?

When a robot arrives back at the depot and “clocks out” for a while, it presents an ideal opportunity to recharge, undergo inspection, run self-diagnostics, download updates, and get the data it needs for its next shift. This is when potentially enormous volumes of data, depending on the fleet size, need to be moved both to and from the device.

Sites that house, charge, or service machinery can therefore find themselves functioning as miniature data hubs, dealing with bursts of traffic as machines arrive and depart. And as fleets grow, so does the strain on the network connecting all these hubs. When uploading and downloading take longer than charging or maintenance, the network becomes the operational bottleneck to scale.

A new layer of connectivity

A wired network simply can’t evolve at the same speed as the mobile economy. A fleet operator might need to open a new depot in another city, place drone nests closer to customers, or connect autonomous equipment at temporary industrial sites. Extending fiber to each location would involve trenching, permitting which are often cost and time intensive.

Wireless connectivity offers a faster and more flexible approach. Links can be installed without digging or waiting for permits, allowing operators to connect new sites quickly. Links can also be redeployed or expanded as fleets grow as their physical footprint expands dynamically.

Wireless optical communication (WOC) could provide that high-capacity bridge. Using narrow beams of light, it carries fiber-like capacity through the air between two points with a clear line of sight, up to 20 Gbps across distances of up to 20 kilometers. It can be installed on existing radio masts or buildings in a matter of hours.

For autonomous fleets, that creates a way to rapidly offload telemetry and operational data, upload new maps and models, and return high-value assets to work instead of leaving them waiting at the depot.

WOC isn’t magic though. Optical links still need line of sight, and heavy fog and rain can degrade their connectivity. That’s why serious deployments pair them with an RF path and engineer them for availability rather than simply assuming it. It won’t replace fiber, but it will extend fiber’s reach to the places fiber can’t go, and it’s getting smaller, faster, and more affordable as the technology moves from mechanical to solid-state beam steering with silicon photonics. Light is the answer.

The arrival of electric vehicles taught us to look beyond the machine and consider the infrastructure behind it. Autonomous machines will require us to do the same again, but this time what needs to be replenished isn’t just the machine’s battery – it’s the machine's experience, and from it, the shared intelligence of the entire fleet.

Robots are becoming extraordinarily good at navigating the physical world. We shouldn’t leave the final mile of their learning journey to someone carrying a hard drive across a parking lot.

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• AI named as one of the three biggest factors to boost productivity
• Visibility over costs and ROI falls short, study finds
• Legacy tech and skills gaps are ongoing issues

Over half (50%) UK CEOs have credited AI with being one of the three biggest contributors to their productivity improvements over the past year, ranking ahead of more traditional aids like redesigning and simplifying business processes (40%) and reselling workers (38%).

But despite naming AI as one of the biggest influencers on productivity, business leaders aren't exactly sure how it's helping, and only 14% of UK CEOs have full real-time visibility over their AI costs, a new EY report has claimed.

As such, one in three (32%) say they're unable to properly asses AI's ROI, making it difficult for them to actually know whether its impacts are translating into financial value.

AI is probably boosting productivity, but we don't really know how

"UK CEOs now want clear evidence that their investment is delivering stronger productivity and measurable business value," UK&I Managing Partner Silvia Rindone explained, implying that companies need greater visibility across their AI spend and impacts.

However, it's not just difficulties quantifying ROI that's helping non-adopters back when it comes to AI. Legacy systems (34%) and organizational complexity (29%) are still considered major blockers four years after generative AI was considered to have gone ultra-mainstream with the public launch of ChatGPT.

Skills shortages also continue to plague businesses, with three in four (74%) UK CEOs anticipating these gaps to worsen over the next three years – more so than access to capital, proving they can't even buy their way out of difficulties.

While organizations are seeking solid evidence that their investments are delivering measurable ROI in terms of productivity and revenue growth, EY's report emphasizes the fact that they can't overlook the same blockers that have been reappearing in studies over the years – legacy tech and skills shortages.

"Realising value still depends on rigorous analysis, clear strategic choices and disciplined execution," UK&I AI & Data Leader Matthew Ringelheim summarized.

• A man fraudulently boosted his music streams using an army of bots
• He was sentenced to 18 months in prison for his actions
• He made over $8 million from the scheme across various platforms

A man has received an 18-month prison sentence after he used a horde of bots to help him rake in $8 million in royalties for his AI-generated music. He’ll have to forfeit all the money he made during his endeavor, which lasted from 2017 to 2024, but he managed to avoid a harsher punishment.

Michael Smith of Cornelius, North Carolina, uploaded hundreds of thousands of songs to streaming platforms, including Spotify, Apple Music and Amazon Music, then used over 1,000 bots to artificially inflate the listening statistics for his tracks to billions of plays. This netted him millions of dollars based on the platforms’ per-stream payment plans.

According to TechSpot, Smith admitted to using 52 cloud service accounts that each had access to 20 bots, putting 1,040 AI agents at his disposal. Connected to VPNs, these bots could stream roughly 636 songs per day, meaning around 661,440 daily plays for Smith.

The US Justice Department (DoJ) put the estimate even higher, saying that Smith deployed up to 10,000 bots at a time. It added that in April 2023, Taylor Swift’s music received 9.3 million plays on YouTube Music. Smith’s music, in contrast, was played 80.9 million times on YouTube Music in the same period.

Smith was charged with wire fraud conspiracy, wire fraud and money laundering conspiracy in September 2024. He pleaded guilty to a single count of conspiring to commit wire fraud in March 2026. He faced a sentence of up to five years’ imprisonment, but in the end was sent down for 18 months and was ordered to forfeit $8,091,843.64. He will also face two years of supervised release at the end of his sentence.

The news comes as Google has announced that it has watermarked 240,000 years’ worth of AI-generated audio and 180 billion AI images and videos using its SynthID Detector tool. Initially available to pro users, SynthID Detector can now be used by anyone and will work with content made by Google, OpenAI, Nvidia and Kakao, with Apple soon following.

Large-scale engagement fraud

It’s important to note that Smith was not prosecuted for making AI-generated music — while many will wish they could banish all AI music from their Spotify feeds, creating tunes this way will not yet get the cops on your case.

Instead, Smith found himself on the wrong side of the law for the way in which he monetized his AI music. By flooding platforms with bot-led artificial engagement, Smith misrepresented the popularity of his tracks and reaped the financial rewards of doing so. As the DoJ put it, “streaming fraud diverts funds from musicians and songwriters whose songs were legitimately streamed by real consumers to those who use automation to falsely create the appearance of legitimate streaming.”

Smith is certainly not the only person artificially boosting their listener counts on music platforms. But when you’re making $8 million from doing so, it’s likely that the authorities will start to pay attention.

Unfortunately, AI has made it absolutely trivial for people to whip up a complete track in seconds, load it onto streaming platforms, then watch the earnings roll in. On the flip side, actually making cash from this kind of endeavor is far from straightforward.

As Vice President of Apple Music Oliver Schusser explained, more than a third of the tracks on the platform are “100% AI,” yet they get “below 0.5% of usage,” meaning barely anyone is listening to AI tunes. But perhaps that reflects a wider problem with streaming platforms: while they might have enormous libraries of music, the vast majority of artists — AI or otherwise — will not see any kind of significant engagement or return.

That’s a broader problem than the one highlighted by Smith’s actions. But with AI making it faster and easier than ever to gain a presence in the world of music streaming, I’m sure we’ll be hearing about more cases like his in the not-so-distant future.

• McDonald’s is being sued over its use of dynamic AI pricing
• The lawsuit alleges it colluded with franchises to set prices
• McDonald’s denies the allegations made in the antitrust case

How would you feel if you got charged more for your McDonald’s meal simply because some opaque AI algorithm decided you should? You’d probably be pretty unhappy, and the practice is now in the spotlight as the fast-food chain is being hit with an antitrust lawsuit over its usage.

The proposed class-action suit alleges that McDonald’s has coordinated pricing between the chain itself and its independent restaurants that operate as part of its franchise system. It claims that, instead of competing among themselves on affordability, McDonald’s franchises are colluding to set prices recommended by AI algorithms, potentially inflating the costs charged to consumers as a result.

According to Reuters, McDonald’s uses its AI algorithm to set prices at roughly 14,000 restaurants in the US and can suggest prices for individual menu items. It considers various data factors, including local purchasing behavior and estimates of the prices consumers will be willing to pay in different locations. But this hasn’t gone down well with everyone.

“Independent businesses must set their prices independently,” the lawsuit states, as per Reuters. Lark Turner, a lawyer ​for ⁠the suit’s plaintiffs, said that McDonald’s is “leveraging its troves of data and its franchised system to nickel-and-dime consumers down to ⁠the last ​French fry.”

The lawsuit argues that McDonald’s and its franchisees have engaged in an unlawful instance of price coordination dating back to 2019. Although it is still in an early stage, it is seeking damages on behalf of potentially millions of consumers.

Future ramifications

McDonald’s has disputed the claims put forth in the lawsuit, saying that “AI does not set the price of a Big Mac ‌or ⁠any other menu item.” It added that franchisees can decide their own menu item prices and that the use of AI to recommend prices is used across multiple industries.

In that last regard, McDonald’s is correct. Firms including Sony, Amazon, Walmart, Best Buy and more often adjust their prices multiple times throughout the day, meaning what you pay in the morning might be different in the evening. This dynamic pricing is powered by AI — and could leave consumers out of pocket if they shop at the wrong time.

This practice might be widespread, but whether that makes it acceptable — or, in the case of the McDonald’s franchises, legal — is another matter, one which this class-action lawsuit could help to decide. As AI is rolled out to almost every facet of daily life, regular consumers have very little visibility over how it affects them and the prices they pay.

Not all fast-food restaurants are rushing to employ AI systems. Chick-fil-A, for example, has refused to install AI in its drive-thru lanes, instead using human employees to take orders. As Chick-fil-A CEO Andrew Cathy told CNBC, “From our experience, we really want that hospitality to be human to human.”

As for the McDonald’s lawsuit, it touches on the issue of where the restaurant chain’s centralized control ends and where local franchise decisions begin. Throw AI into the mix and you’ve got a heady mix of traditional antitrust legislation and emerging AI tech that could have ramifications far beyond the restaurant industry.